{"as_of":"2026-08-10T23:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e3ccfba7a1ed3e47e44c60b48e246cbc6d747dfec65e15359a144e0f8743453","coverage":[{"denominator":127,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:57:29.332000Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.12732/citation-record","integrity":"/paper/2501.12732/integrity","json":"/paper/2501.12732/citation-record.json","paper":"/paper/2501.12732"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:28.986649Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:28.986649Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:5ad609b9837b68f61054cfdc198ea537cc80baa5f00c6d9020bbd0daa6d649d8","observation_id":"cea55fb2-5eee-4cb8-91db-4150a4d68e4f","resolution":{"observed_at":"2026-08-10T16:57:28.986649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:28.991908Z","title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:28.991908Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:71dc7fc18746d487eb5fc397361973b27b1fe241e27bf754b1d3f698d24ad90e","observation_id":"d9c805ae-134f-466f-bbc2-0086881b67b6","resolution":{"observed_at":"2026-08-10T16:57:28.991908Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:28.995760Z","title":"On the bottleneck of graph neural networks and its practical implications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:28.995760Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:39133790ca6cda1674ef41488976a50c57f4e4f3e1f07a86809747fb4b684fc3","observation_id":"bbe6d8ad-1737-448d-8aff-fb93fd3ae2ea","resolution":{"observed_at":"2026-08-10T16:57:28.995760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:28.999295Z","title":"State Space Models: A Unifying Framework","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:28.999295Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:bac4c0d6d89591e7b88d0d569753f57dedf24a23ea8f397da7118e8f9616cd09","observation_id":"1139cc3c-9af1-40e6-bb9b-6cb67ef01ff8","resolution":{"observed_at":"2026-08-10T16:57:28.999295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.003017Z","title":"Unitary evolution recurrent neural networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.003017Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:dbb67a68b11b8ce24d96f9bac1ecadb40a60e5cc31450c4f32198513c7accb44","observation_id":"c3520d6b-df8c-4e91-a4ad-ced84f7aaaf2","resolution":{"observed_at":"2026-08-10T16:57:29.003017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.006807Z","title":"Accurate prediction of protein structures and interactions using a three-track neural network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.006807Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:97caaa102afee6267188473a13e9e72165f9f6ab077a0d24dc0472c405806c8a","observation_id":"e7a390ba-a278-418c-9b3d-c54a47281bfe","resolution":{"observed_at":"2026-08-10T16:57:29.006807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/ijgi10070485","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.496568Z","title":"A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting","venue":null,"work_id":"79f5373e-edb0-482b-9da7-b924bec30e2c","year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.010584Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:98753ca50bc05b376beb2f11616c12c0c846a89e07eb1f0fb127e47f0abcee0a","observation_id":"1a50ede3-487f-4ca0-bfa9-8989e98785f6","resolution":{"observed_at":"2026-08-10T16:57:29.500511Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.04517","last_updated":"2024-12-06T15:42:07Z","snapshot_observed_at":"2026-08-07T15:21:45.322877Z","submitted_at":"2024-05-07T17:50:21Z","title":"xLSTM: Extended Long Short-Term Memory","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.04517","snapshot_observed_at":"2026-08-10T16:57:29.015396Z","title":"o ppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, G \\","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.015396Z"},"links":{"cited_paper":"/paper/2405.04517","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:6a544ae1e667f8283e91194a33ab4e9e749410f4e1abb46c457a9ae15861bd77","observation_id":"1b48645e-49ed-4046-89f6-2a06dce5b447","resolution":{"observed_at":"2026-08-10T16:57:29.015396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08678","last_updated":"2024-02-19T18:53:13Z","snapshot_observed_at":"2026-08-06T16:35:04.650921Z","submitted_at":"2024-02-13T18:58:17Z","title":"Graph Mamba: Towards Learning on Graphs with State Space Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08678","snapshot_observed_at":"2026-08-10T16:57:29.019297Z","title":"Graph Mamba: Towards Learning on Graphs with State Space Models , 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.019297Z"},"links":{"cited_paper":"/paper/2402.08678","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3724f6b1d3424ca4a7b9b53427015eeafae28a99126e1c0bb3246597c18f62c9","observation_id":"3172955f-6e79-431c-bc0a-6356eb2ed1aa","resolution":{"observed_at":"2026-08-10T16:57:29.019297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.023434Z","title":"Learning long-term dependencies with gradient descent is difficult","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.023434Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:bb8bd0091c7dfdaf90853dc821c6e4ad736b1152ba6e518f14d25519894c7961","observation_id":"e99d6082-4897-438a-808a-cadbaba6964f","resolution":{"observed_at":"2026-08-10T16:57:29.023434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.027187Z","title":"Graph neural networks with convolutional arma filters","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.027187Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3db0add3a63ee1498ba8928bea2db80732520f2192ffcd32e8061170b48f5462","observation_id":"f9cd668b-6eef-45e7-9b51-4f5488e8d875","resolution":{"observed_at":"2026-08-10T16:57:29.027187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.030886Z","title":"Beyond low-frequency information in graph convolutional networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.030886Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e6e190621822812d9b1c86e04cb26a526be7c38fb769a7bd3622d94bec224a07","observation_id":"3e893fcf-0e37-42c9-9488-c8779b77d4b4","resolution":{"observed_at":"2026-08-10T16:57:29.030886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.034809Z","title":"Improving graph neural network expressivity via subgraph isomorphism counting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.034809Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:9abe29add882f2f4e67bc32f03a0071f2c29cbd6dca607f34f1b90aafc982473","observation_id":"2369868e-d972-4324-9595-3be2c6f4bbaf","resolution":{"observed_at":"2026-08-10T16:57:29.034809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.038413Z","title":"Time Series Analysis: Forecasting and Control","venue":null,"work_id":null,"year":1970},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.038413Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:d4d86452224d7671916c6e38334e6b040a1e1552da70ed7450d2813335b10786","observation_id":"b259476e-d013-4675-bc9c-7a3607781a61","resolution":{"observed_at":"2026-08-10T16:57:29.038413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.07553","last_updated":"2018-04-24T08:19:32Z","snapshot_observed_at":"2026-08-09T01:06:08.452911Z","submitted_at":"2017-11-20T21:28:40Z","title":"Residual Gated Graph ConvNets","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.07553","snapshot_observed_at":"2026-08-10T16:57:29.042022Z","title":"Residual Gated Graph ConvNets","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.042022Z"},"links":{"cited_paper":"/paper/1711.07553","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:a40fcc93405562a5ac7d40ce59ca317ec32fe1ca5d4d4db3f843607438a4e76a","observation_id":"71b5dbcc-959d-4c8a-8141-a750bf5efeed","resolution":{"observed_at":"2026-08-10T16:57:29.042022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.13478","snapshot_observed_at":"2026-08-10T16:57:29.046073Z","title":"Geometric deep learning: Grids, groups, graphs, geodesics, and gauges","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.046073Z"},"links":{"cited_paper":"/paper/2104.13478","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:c8dbe539c51a1fbf670b18b8db9a8a95f9b6947e3cbe98ead12dc1f353b1a86e","observation_id":"80879b35-6f70-43ef-bb10-00fd922b619c","resolution":{"observed_at":"2026-08-10T16:57:29.046073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13318","last_updated":"2020-06-23T20:36:56Z","snapshot_observed_at":"2026-07-06T09:32:05.177891Z","submitted_at":"2020-06-23T20:36:56Z","title":"A Note on Over-Smoothing for Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13318","snapshot_observed_at":"2026-08-10T16:57:29.049782Z","title":"A note on over-smoothing for graph neural networks","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.049782Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:745b9a32234d0eaec23ab2cd40c476b27689ba9a107ec0eabb3d1e0158a3312f","observation_id":"2f13268f-4244-4bc2-8e2a-7d903a83346c","resolution":{"observed_at":"2026-08-10T16:57:29.049782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.054185Z","title":"GRAND : Graph neural diffusion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.054185Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:278cff1014f634bc66592e99b3c0ae303de20ecfede28213c1d60ae18d97e70a","observation_id":"820f7714-0013-4a74-a363-2d5f94e8f5c8","resolution":{"observed_at":"2026-08-10T16:57:29.054185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.057977Z","title":"Simple and Deep Graph Convolutional Networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.057977Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:349fa0c59739d36d33b6e541e53c5e722e9c989b8a72620cebd821ac60d9813c","observation_id":"3e1bcf35-fe93-49a4-ad41-832a41df5826","resolution":{"observed_at":"2026-08-10T16:57:29.057977Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.061610Z","title":"Neural ordinary differential equations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.061610Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e564da789fda3ac50951285504bf897a6654245ea7ad8ac2d1959363a9a5a596","observation_id":"4ec8ebfb-e376-464a-ba2d-c19efed97a71","resolution":{"observed_at":"2026-08-10T16:57:29.061610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.065263Z","title":"Adaptive universal generalized pagerank graph neural network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.065263Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:1971a8408fa4fb57663256926937126a8a969a12c19dadb7b4e6bb30997dc138","observation_id":"6a9337bd-40b3-41ef-8616-438975a451ff","resolution":{"observed_at":"2026-08-10T16:57:29.065263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.068758Z","title":"Gread: Graph neural reaction-diffusion networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.068758Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:c0b0c5cdc3a550511abaf2a5954210a8e6c9b8d52e8a73f68510a9d3a8512b60","observation_id":"00a28f5f-7dca-417c-b4e6-4ba408050eea","resolution":{"observed_at":"2026-08-10T16:57:29.068758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14743","last_updated":"2020-09-30T15:29:05Z","snapshot_observed_at":"2026-08-10T19:25:05.008447Z","submitted_at":"2020-09-30T15:29:05Z","title":"Multi-channel Deep 3D Face Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14743","snapshot_observed_at":"2026-08-10T16:57:29.071737Z","title":"Beletsky, Konrad M","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.071737Z"},"links":{"cited_paper":"/paper/2009.14743","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:15c24416b1bac9b467061e6272ed3a770ef413335748fbc066ce2064512b83e6","observation_id":"00e7215d-5213-4bda-8caa-f927cc5229f9","resolution":{"observed_at":"2026-08-10T16:57:29.071737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.075134Z","title":"From block-toeplitz matrices to differential equations on graphs: towards a general theory for scalable masked transformers","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.075134Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:fc5414d24d1b80812dd8ae5589aab6cb8624595e9fcafc17661ac64ea0b6ce48","observation_id":"006f7973-30b9-4b28-bffe-9513d6532066","resolution":{"observed_at":"2026-08-10T16:57:29.075134Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19427","last_updated":"2024-02-29T18:24:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-29T18:24:46Z","title":"Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19427","snapshot_observed_at":"2026-08-10T16:57:29.078058Z","title":"Griffin: Mixing gated linear recurrences with local attention for efficient language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.078058Z"},"links":{"cited_paper":"/paper/2402.19427","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:db6762a0c7e8688264f611cfe9ece8a3e453dd9a9aa9e5f1eb58eb8190336697","observation_id":"64bd7a44-1de4-491a-963a-53bee77dd574","resolution":{"observed_at":"2026-08-10T16:57:29.078058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.082397Z","title":"The arma model in state space form","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.082397Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:2fe4f7e37f50973926e1dbd674bed3e38afaeaab547a392ed3c59cd9d39a74ef","observation_id":"d92c8bed-08e1-4eaa-9dca-bcc83f6b1836","resolution":{"observed_at":"2026-08-10T16:57:29.082397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.085436Z","title":"Polynormer: Polynomial-expressive graph transformer in linear time","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.085436Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:2baae3ab239ad9226bbb3c239b17bb9c3dc5b7bf7fdda28dc38669773b59c604","observation_id":"95daaed0-9cff-4e2a-b246-63e4341fe027","resolution":{"observed_at":"2026-08-10T16:57:29.085436Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.088366Z","title":"On over-squashing in message passing neural networks: the impact of width, depth, and topology","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.088366Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:a372d3220ee3c1a6eaf774127c56b8503ed457ba14e3c67cc2d1de091ba28bc9","observation_id":"7351b26f-79d9-4569-84d4-13deaee3d3e1","resolution":{"observed_at":"2026-08-10T16:57:29.088366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.092146Z","title":"Gbk-gnn: Gated bi-kernel graph neural networks for modeling both homophily and heterophily","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.092146Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:af90c450206f81b4a7a5ba5baca77a432b46d43ac60f9434d558e5cac6793af9","observation_id":"4fd08b4a-3edd-49e6-9b0a-3a5080789a99","resolution":{"observed_at":"2026-08-10T16:57:29.092146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.095885Z","title":"Dwivedi and X","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.095885Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:0ab2aa43c4a72f1652dc64943a7c1df31544a5be66bacfce10cd03ca4af2dd45","observation_id":"affd27fa-20dc-4dcd-98f5-70861e0ea3df","resolution":{"observed_at":"2026-08-10T16:57:29.095885Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.099403Z","title":"A Generalization of Transformer Networks to Graphs","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.099403Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:1388612fd8dc89e15134ae0048c534aabd768462ea1bb16d1626a495567335d6","observation_id":"20a9909f-827f-443b-a257-503d6c421353","resolution":{"observed_at":"2026-08-10T16:57:29.099403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.103299Z","title":"Graph neural networks with learnable structural and positional representations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.103299Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:fcd61f8119fce4fb2f5d3d02ec894d5d787e8387e57580e5e506db9fb7d22424","observation_id":"c088d1bb-a362-46c9-bbe3-25ce5805d666","resolution":{"observed_at":"2026-08-10T16:57:29.103299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.107190Z","title":"Long Range Graph Benchmark","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.107190Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:36d44378f01c795c41c35b5f582dd99f66ddae7e665db918ecb0a5f9f193894a","observation_id":"3ab769ad-fbb5-4333-8ce0-febf8e66f43e","resolution":{"observed_at":"2026-08-10T16:57:29.107190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.111329Z","title":"Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.111329Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:93068618cd6803431e3930e46fa06e9dd9bf1eaf77d8fed72bcfcf7d51ee2a41","observation_id":"47de6ac8-525d-4681-a126-c01f374c572b","resolution":{"observed_at":"2026-08-10T16:57:29.111329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.114929Z","title":"Benchmarking graph neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.114929Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:13851e6a2f925d19b8d32c8fe63d422824722545f7133fb870f410aae2563d3c","observation_id":"50db1317-2fa4-44f7-87bb-2f3da574f158","resolution":{"observed_at":"2026-08-10T16:57:29.114929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.118476Z","title":"PDE-GCN : Novel architectures for graph neural networks motivated by partial differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.118476Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e3de3a7324979cd9bc54ba331703cec997f13e8d1927759490419205b23fcca8","observation_id":"1e977b4b-3ff7-4080-ae49-3c065f2f0237","resolution":{"observed_at":"2026-08-10T16:57:29.118476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.13344","last_updated":"2024-10-31T23:12:29Z","snapshot_observed_at":"2026-07-06T18:03:02.200489Z","submitted_at":"2024-04-20T10:44:13Z","title":"GRANOLA: Adaptive Normalization for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.13344","snapshot_observed_at":"2026-08-10T16:57:29.121962Z","title":"Granola: Adaptive normalization for graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.121962Z"},"links":{"cited_paper":"/paper/2404.13344","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:80a7ba8b78c37d11fb890ba5177264844ac3f49dc13252d223a2b132d8e8fa50","observation_id":"fbc5531c-661b-4e89-a162-a27fe7aad845","resolution":{"observed_at":"2026-08-10T16:57:29.121962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.125814Z","title":"On the temporal domain of differential equation inspired graph neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.125814Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:125019cf85aa784daee3e76d76bc1b377512781830ab682e5d99671d07720c85","observation_id":"96d38684-b0a8-4c1e-b4d5-e2a06c3ca4b3","resolution":{"observed_at":"2026-08-10T16:57:29.125814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.129138Z","title":"Bronstein, and Ismail Ilkan Ceylan","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.129138Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:de9a4e9dd07c8bb36fbc723fe8a2f74d922d7e907799aa7735204b7c0475ba88","observation_id":"1943116e-7f00-4277-abe0-c2a8a767317b","resolution":{"observed_at":"2026-08-10T16:57:29.129138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.132694Z","title":"A large-scale database for graph representation learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.132694Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:0f1733d61c51ff29b6da32bfef6e00c20875c8b77f93b236539e7efb16ec2e89","observation_id":"cd38571b-66ee-41fd-a2c3-24f02358d3d6","resolution":{"observed_at":"2026-08-10T16:57:29.132694Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.135994Z","title":"S4: Structured state space for scalable and efficient sequence modeling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.135994Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:4f8dfc4e3880536112ff786d274f5c3162c79dd2b7d7e49f378fdb9bbdc86c2a","observation_id":"097a2afb-7778-49cb-ae13-e8a9a200176b","resolution":{"observed_at":"2026-08-10T16:57:29.135994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.139655Z","title":"Diffusion Improves Graph Learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.139655Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:f4841fa7cdc9954a56725ba75243f8d20b0f7736c57c1dedfe70562e7bfd943e","observation_id":"851b2960-b617-4dc4-96ed-b39151143ed7","resolution":{"observed_at":"2026-08-10T16:57:29.139655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.143109Z","title":"Anti-Symmetric DGN: a stable architecture for Deep Graph Networks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.143109Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:39124c2b108b24642dcf0cd084a41d91589a983becc758dd88abb320b1092201","observation_id":"c1a8237f-a818-4bcb-af67-f039dabcd84e","resolution":{"observed_at":"2026-08-10T16:57:29.143109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.01009","last_updated":"2025-02-28T10:37:29Z","snapshot_observed_at":"2026-08-09T20:12:48.190458Z","submitted_at":"2024-05-02T05:23:58Z","title":"On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01009","snapshot_observed_at":"2026-08-10T16:57:29.146615Z","title":"Tackling Oversquashing by Global and Local Non-Dissipativity","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.146615Z"},"links":{"cited_paper":"/paper/2405.01009","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3ef55bcc9356f9803d431e8b4938cb9973670a99098467e8e6a5d5590dd43b6b","observation_id":"86dde8b2-1ef1-45b0-bd6d-d02a8c0fcc90","resolution":{"observed_at":"2026-08-10T16:57:29.146615Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24963/ijcai.2024/445","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.475341Z","title":"Temporal graph odes for irregularly-sampled time series","venue":null,"work_id":"1e7cf993-ac9b-41fd-a520-8a54af3f90bb","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.150373Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:9c8cdf0f2f069a34889675ee61b575edb60e38ae28ff2decf640b81e13087034","observation_id":"939065bb-b560-493f-baa4-1addcdbb4947","resolution":{"observed_at":"2026-08-10T16:57:29.481130Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2111.00396","last_updated":"2022-08-05T17:54:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-10-31T03:32:18Z","title":"Efficiently Modeling Long Sequences with Structured State Spaces","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00396","snapshot_observed_at":"2026-08-10T16:57:29.153875Z","title":"Efficiently modeling long sequences with structured state spaces","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.153875Z"},"links":{"cited_paper":"/paper/2111.00396","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:31c2ceb573d075d6c9eea3fa76dae69f7b3c5f89d4d492864ba1043c1e478b49","observation_id":"43d499f2-3733-4c94-b79d-83f158dacfaa","resolution":{"observed_at":"2026-08-10T16:57:29.153875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.157796Z","title":"Combining recurrent, convolutional, and continuous-time models with linear state space layers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.157796Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:8d1866d023956f808aca70181e9e608b80fe0a6e77342b67652f851428dfe425","observation_id":"6afdeb92-85a1-46ef-a824-f2dce68db729","resolution":{"observed_at":"2026-08-10T16:57:29.157796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.161266Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.161266Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:37ddb0497822cb0aea032f2cc2c5489c8af1c239fb63c5600e6dce26680341e7","observation_id":"6172d1e7-9050-468f-8696-ef6d7d774eab","resolution":{"observed_at":"2026-08-10T16:57:29.161266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.164647Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.164647Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:ca6697d7bcfac99f79870729fc595d4325fe37c008c0568f766701fe4d61c307","observation_id":"664f4d07-7afb-434b-b43b-733083a1ce5c","resolution":{"observed_at":"2026-08-10T16:57:29.164647Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.168418Z","title":"Drew: Dynamically rewired message passing with delay","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.168418Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:cef28d395133d059c06323226dacea4b3bd4993bc19e3dfe4c484a3c3e95a8ac","observation_id":"61b83c2a-6264-4380-9422-db937a167e4a","resolution":{"observed_at":"2026-08-10T16:57:29.168418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.172956Z","title":"Hamilton","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.172956Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:cdee20b5fe5e64890151277c64a0f55f70bb812ae882427eaa2505f700ec4dac","observation_id":"18d29c72-b1e3-4e2d-90a8-ed3dc71ace4f","resolution":{"observed_at":"2026-08-10T16:57:29.172956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.176352Z","title":"State-space models","venue":null,"work_id":null,"year":1994},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.176352Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:c0cd56392831a883b9bc40f1a88beaa4985ea414b414de9d7633ddc17d872844","observation_id":"6453531c-e890-47a6-aa4e-3368dcd5625a","resolution":{"observed_at":"2026-08-10T16:57:29.176352Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.179790Z","title":"Hamilton, Rex Ying, and Jure Leskovec","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.179790Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:272c12f6962f93a506e32008dcee9771a723ac5d9f43d7926d18d8264e647e7d","observation_id":"56971d0c-fc6b-44d5-97ab-a715dc6e7ad8","resolution":{"observed_at":"2026-08-10T16:57:29.179790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.183138Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.183138Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:fcdf85325269f4636f4135d5cd30196594115327259104a6263bd09b8331e2b3","observation_id":"974ea53c-bc8c-4b22-b469-7b143d72e2d7","resolution":{"observed_at":"2026-08-10T16:57:29.183138Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.186451Z","title":"Bounding the roots of polynomials","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.186451Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e3455b285bbc7c88a1550c155406959662a3d5d87e72e032fcc0af753042311c","observation_id":"4c6e0927-d905-424a-a06e-6b51fa6b1e06","resolution":{"observed_at":"2026-08-10T16:57:29.186451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.189414Z","title":"Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.189414Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:a168629cc899bf37eb3b55e8f040945fa5c4e2bd879ef0142d898da21b918e78","observation_id":"497c967e-74ba-40dc-bb6b-ea582bd3d74c","resolution":{"observed_at":"2026-08-10T16:57:29.189414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.192371Z","title":"Matrix analysis","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.192371Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:60d3d0cf32b08f511ceb5e4762b0f2a2966f610673a5eff9067427e4a5cfc04e","observation_id":"d186a465-720a-44de-b012-f548067f0fa4","resolution":{"observed_at":"2026-08-10T16:57:29.192371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.195447Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.195447Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3383ced5897887afa8037dcaff0f42a6021e2ceaea584537f2be51b8ac28abdd","observation_id":"e7576867-e141-46e1-b4bc-3b44fef7d57c","resolution":{"observed_at":"2026-08-10T16:57:29.195447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.198226Z","title":"Strategies for Pre-training Graph Neural Networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.198226Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:1f880b7e9e5c44730974eb924b4dc985b9061ce0f6009f1e88ee68450a8b38b6","observation_id":"0d917384-268e-4fc0-aeba-3a444da399c8","resolution":{"observed_at":"2026-08-10T16:57:29.198226Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.201011Z","title":"Densely connected convolutional networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.201011Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:2961e33a1c125b2dd4390d8a2635d7cb47af1c58c3ea2f972d22d2d7505a543e","observation_id":"ca31225e-5a8d-4e60-8b56-e3bfb56de5f2","resolution":{"observed_at":"2026-08-10T16:57:29.201011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.05815","last_updated":"2024-10-04T08:46:03Z","snapshot_observed_at":"2026-07-06T18:27:45.631190Z","submitted_at":"2024-06-09T15:03:36Z","title":"What Can We Learn from State Space Models for Machine Learning on Graphs?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.05815","snapshot_observed_at":"2026-08-10T16:57:29.203919Z","title":"What can we learn from state space models for machine learning on graphs? arXiv preprint arXiv:2406.05815, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.203919Z"},"links":{"cited_paper":"/paper/2406.05815","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:0bdc3dfd10c5a774009f0ab671c8cb041390d6ede880207467469012cb9b445c","observation_id":"76b6bfa0-6442-4a9c-86fe-4bc663ab0918","resolution":{"observed_at":"2026-08-10T16:57:29.203919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.206882Z","title":"Autoregressive moving average graph filtering","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.206882Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:966bf1ef0fe3ee77f27938ed54aa3addc50da73ed36d40553acc8ea83f5fced3","observation_id":"c6f4e751-5b93-4908-b94a-0e2155b022c9","resolution":{"observed_at":"2026-08-10T16:57:29.206882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.677812Z","title":"Unleashing the potential of fractional calculus in graph neural networks with FROND","venue":null,"work_id":"71e27043-c6c0-48d6-adeb-60735325c096","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.209599Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:0c122326e6b062977e12eaa9a43d31105582975b7070224d9789c8d27237d699","observation_id":"54b40569-94e7-42b7-bfd8-9c1c7d8cd41d","resolution":{"observed_at":"2026-08-10T16:57:30.682046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.666456Z","title":"Banerjee, and Guido Montufar","venue":null,"work_id":"75a578ed-d39d-4c90-98d3-a455e2b224df","year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.212395Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:085cdba3aa55ca21ef6f84bbabe1c4e4b9865e2d329ce239db8f5ef683a75ff3","observation_id":"7e03b3f0-41c6-494e-8b4e-ca10d316aee4","resolution":{"observed_at":"2026-08-10T16:57:30.670153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.656695Z","title":null,"venue":null,"work_id":"5a5fdb6b-0be0-47ed-96ed-3cb7b379a4d0","year":2002},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.215647Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:a16e4d476717247925a39aa718f84df96b1a5c16ad1ecd1a34b96c5ba7e66ac4","observation_id":"ccef2f44-58f1-4b15-b84f-d791a81fdde1","resolution":{"observed_at":"2026-08-10T16:57:30.659876Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.646341Z","title":"A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions","venue":null,"work_id":"172dbe7d-70a5-4aea-bcde-c2a2feb6fcc3","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.219297Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:d859bad0adb36bd573aea6d3e26bcbc11e430473385c4a9dd58e6b3b2c1380d7","observation_id":"d39d0886-ed0a-4d7d-86b6-275ceb97597e","resolution":{"observed_at":"2026-08-10T16:57:30.650172Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.222805Z","title":"Kipf and M","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.222805Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e884251d80c6ce8e7907495a24cba35ac680c04ad04c6bfb1dd81e51e0b54557","observation_id":"aba591e2-fd4c-478f-92cf-38e3bb99e8de","resolution":{"observed_at":"2026-08-10T16:57:29.222805Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.628658Z","title":"Bayan Bruss, and Tom Goldstein","venue":null,"work_id":"2134a138-eef1-45dd-bd7f-b9be9e72a9cd","year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.226139Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:ffabaafc4a0d622f6cb214c68acef0e10cd12c35a1fe3403683149f38a10e7db","observation_id":"aad84fb9-8fcf-4e9f-9f30-61c09ea8811e","resolution":{"observed_at":"2026-08-10T16:57:30.632293Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.229512Z","title":"Rethinking graph transformers with spectral attention","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.229512Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:c44f3213b2a1c91d90ff88c585b78bd0a2695c6c35a370263889613cbf99d9fa","observation_id":"fd11f042-7bf8-475d-9e8b-f6679cb72984","resolution":{"observed_at":"2026-08-10T16:57:29.229512Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.611353Z","title":"Kreuzer et al","venue":null,"work_id":"cf2c721f-e0ad-40db-9f7f-071fb4b0aa4d","year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.232810Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:b6a1b56c785df6e65115928c01dc497878843f7b5a63b24b9a7df01ede0d626d","observation_id":"539cbe84-fb4d-4f74-b8a3-441ebca3f6c7","resolution":{"observed_at":"2026-08-10T16:57:30.614868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.600323Z","title":null,"venue":null,"work_id":"48a5d490-ad28-4a30-a7f9-0c89a48b86ec","year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.235872Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:33f10e6ecd197f48d776ba5350f208429928dec1cbe35ed1c24296bd33778c57","observation_id":"19bfeea3-f216-4677-9381-e68a7854e7bd","resolution":{"observed_at":"2026-08-10T16:57:30.603850Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.588711Z","title":"Finding global homophily in graph neural networks when meeting heterophily","venue":null,"work_id":"301f97c4-b59b-472b-a6cf-9cd4dc68a0e1","year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.239018Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:04ab13dc80309f882733c8a4e5494175c64f7a22304a6f0d2caf7fc9d50ceabb","observation_id":"268d523d-9184-488f-8fb3-102cd1492d00","resolution":{"observed_at":"2026-08-10T16:57:30.592579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.242269Z","title":"Toloker Graph: Interaction of Crowd Annotators , February 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.242269Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e05eb2d38fee916ef62de6ce825f1fff91f9bdb765116267281711afac4db036","observation_id":"cc4fcfc0-42b4-4548-a9e4-e33da8174b03","resolution":{"observed_at":"2026-08-10T16:57:29.242269Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.577795Z","title":"Mamba: Beyond long sequences","venue":null,"work_id":"392c818d-24d8-4040-a1cc-f490c5f00f53","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.246038Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:efb6016132c67678ca293e5b353f6f8049e01eac77d6bee2b2552a341d33f9d5","observation_id":"c332cc6c-2365-4f50-bfe7-9f8bc3e7f879","resolution":{"observed_at":"2026-08-10T16:57:30.581396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.09618","last_updated":"2024-07-12T18:04:32Z","snapshot_observed_at":"2026-08-06T01:25:59.316954Z","submitted_at":"2024-07-12T18:04:32Z","title":"The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.09618","snapshot_observed_at":"2026-08-10T16:57:29.249359Z","title":"Li, Jian Tang, Guy Wolf, and Stefanie Jegelka","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.249359Z"},"links":{"cited_paper":"/paper/2407.09618","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3f63efa78d86466d2b482caad8cd5d1c01727681f8f01890043ca3c942e60583","observation_id":"eb1069b7-e0ba-4f4d-b9d6-dfa85f8c57b6","resolution":{"observed_at":"2026-08-10T16:57:29.249359Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02013","last_updated":"2024-10-30T22:58:26Z","snapshot_observed_at":"2026-07-30T21:27:41.977497Z","submitted_at":"2024-07-02T07:33:40Z","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","version":2},"cited_work":{"arxiv_id":"2407.02013","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.02013","snapshot_observed_at":"2026-08-10T16:57:29.983401Z","title":"DiGRAF: Diffeomorphic Graph-Adaptive Activation Function","venue":"cs.LG","work_id":"dd1a8db1-9cd4-403b-8a5c-74c1f51a06b0","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.252941Z"},"links":{"cited_paper":"/paper/2407.02013","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:1a7840b32f9a510435ed98ca35753aecc0cbf6159093a260a44c4b85cd332b77","observation_id":"da0f2e28-befd-4dd4-af40-c381a6248d6f","resolution":{"observed_at":"2026-08-10T16:57:29.986856Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.256389Z","title":"A fractional graph laplacian approach to oversmoothing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.256389Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:6cebd68ff8a8e1dd647931cb33f51baa41f056c3862cf25cd08e3fc86dc51c29","observation_id":"d4634d97-ff7f-4923-b131-f97c873dd599","resolution":{"observed_at":"2026-08-10T16:57:29.256389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.259677Z","title":"Simplifying approach to node classification in graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.259677Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:4e236ba0eeb090e5995e670fb23a6020cd8c220e7f1c3ba26d13135de30719a7","observation_id":"a13040a8-cb02-483b-8764-247b85295fcd","resolution":{"observed_at":"2026-08-10T16:57:29.259677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.559680Z","title":"Weisfeiler and leman go neural: Higher-order graph neural networks","venue":null,"work_id":"be2137c2-01cc-47ee-ba87-368b5c760c91","year":2019},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.263031Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:029ea08a81e94b4f440d6fb5555d7ffcd4bbdc8315b80ce9b72737ff932fa3f6","observation_id":"3a1e4999-8f5b-4d64-b01c-bccf515ad3cd","resolution":{"observed_at":"2026-08-10T16:57:30.563567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.550048Z","title":"Attending to graph transformers","venue":null,"work_id":"6252e834-62c9-46ee-be87-19e037879ea6","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.266323Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:886fcedfabedf62aa265d28deab009ec392e415317bb6e73070382fd1e9dd6d1","observation_id":"b666f0da-a06f-4c25-bacb-3679a60e3b0d","resolution":{"observed_at":"2026-08-10T16:57:30.553299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.539230Z","title":"Nguyen et al","venue":null,"work_id":"1e5346ac-4097-495b-b0e7-c1994624a33d","year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.269661Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:3fe0353761a2802c5a0416f7873abde2b444d609d17abe6bd5512806631d1112","observation_id":"be0226fa-0ff6-4734-9aff-1acfce349bea","resolution":{"observed_at":"2026-08-10T16:57:30.543373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.09550","last_updated":"2019-05-26T05:21:04Z","snapshot_observed_at":"2026-07-06T07:54:54.802338Z","submitted_at":"2019-05-23T09:27:21Z","title":"Revisiting Graph Neural Networks: All We Have is Low-Pass Filters","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.09550","snapshot_observed_at":"2026-08-10T16:57:29.273197Z","title":"Revisiting graph neural networks: All we have is low-pass filters","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.273197Z"},"links":{"cited_paper":"/paper/1905.09550","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:cdd9e77bdc24d1a3606679b6adbe88b8c3a9f41e11040041db2884393d7148ab","observation_id":"33a80971-89b9-4ede-b5f8-4a68d2b2d7d5","resolution":{"observed_at":"2026-08-10T16:57:29.273197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.276774Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.276774Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:826e8d4f399ef1e8ef762d19f02f61d68df11788e2c1b1898c5e79daa6ad83d5","observation_id":"857c63ed-d665-4f45-aa58-b8003ef92439","resolution":{"observed_at":"2026-08-10T16:57:29.276774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.11888","last_updated":"2024-06-05T10:00:40Z","snapshot_observed_at":"2026-08-05T17:27:18.136855Z","submitted_at":"2023-07-21T20:09:06Z","title":"Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.11888","snapshot_observed_at":"2026-08-10T16:57:29.280109Z","title":"On the universality of linear recurrences followed by nonlinear projections","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.280109Z"},"links":{"cited_paper":"/paper/2307.11888","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:47153f88545e550081bec977c7a9669662c8e5d8f1606f4c107ae18b17fbd644","observation_id":"a0362733-5b59-49fa-a4a4-b6fc9617f2e6","resolution":{"observed_at":"2026-08-10T16:57:29.280109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.521557Z","title":"Resurrecting recurrent neural networks for long sequences","venue":null,"work_id":"e24e79e8-94bd-40c7-8353-f937aed98a32","year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.284075Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:20028900075715f949f414c575626cd55320dd26298cfd79d0741cb52c9b7e17","observation_id":"8d561eb9-2c98-4308-8b0a-d2803246efa5","resolution":{"observed_at":"2026-08-10T16:57:30.525312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.510683Z","title":"Permutation equivariant layers for higher order interactions","venue":null,"work_id":"3ce83af4-ae48-4e61-a0d8-834bb2377eb7","year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.287619Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:492d096198a542b0d39d9b89aaa3b5605d93c623c80a7c040bb2058e8fe6ab8f","observation_id":"30e959f8-45bc-47e1-8da5-4d6a29e78fdb","resolution":{"observed_at":"2026-08-10T16:57:30.514384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1211.5063","last_updated":"2013-02-16T00:35:48Z","snapshot_observed_at":"2026-08-07T12:06:37.739332Z","submitted_at":"2012-11-21T15:40:11Z","title":"On the difficulty of training Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1211.5063","snapshot_observed_at":"2026-08-10T16:57:29.291048Z","title":"On the difficulty of training recurrent neural networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.291048Z"},"links":{"cited_paper":"/paper/1211.5063","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:804567b310f0d6f9489e7283af0fff5e41183f2aefdd28d89301d1369954ac4a","observation_id":"00806e71-dbd9-419c-8dab-2ba2d6a2cbbb","resolution":{"observed_at":"2026-08-10T16:57:29.291048Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.294638Z","title":"Pytorch: An imperative style, high-performance deep learning library","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.294638Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:e5ed311216091eec4f69b09d91374135dd9af7d4a047f5011aa399ff945b1eaa","observation_id":"9685610f-4905-4335-9191-53f4c6e2095c","resolution":{"observed_at":"2026-08-10T16:57:29.294638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.492398Z","title":"Geom-gcn: Geometric graph convolutional networks","venue":null,"work_id":"f0cf24e1-93f7-4a76-a9df-2e12530b658f","year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.297999Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:c50f8c8cbe8f7466e17612fd7a67cc5808039b5e9a2b5e15f396d50dac8a7f68","observation_id":"82964e13-d986-4e0b-925e-140c2e3917a3","resolution":{"observed_at":"2026-08-10T16:57:30.496464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.480138Z","title":"A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In The Eleventh International Conference on Learning Representations, 2023","venue":null,"work_id":"3d9e4bad-7357-4e3a-8996-d8b90422e30e","year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.300964Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:4607863ffae9e708f504254ba37850a40cba283221f48b002a5a59efcf43c09c","observation_id":"7a970a88-9b43-45b7-a1ee-a93e5a910b4b","resolution":{"observed_at":"2026-08-10T16:57:30.484342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.07532","last_updated":"2021-06-22T07:40:01Z","snapshot_observed_at":"2026-08-08T07:19:01.554491Z","submitted_at":"2019-11-18T10:46:15Z","title":"Graph Neural Ordinary Differential Equations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.07532","snapshot_observed_at":"2026-08-10T16:57:29.303679Z","title":"Graph neural ordinary differential equations","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.303679Z"},"links":{"cited_paper":"/paper/1911.07532","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:20396937151407b5363f0ca2543501b57b84814c8fe1b7e2181e297c336e0ab1","observation_id":"3996a4df-e669-4400-9de5-a386c2315615","resolution":{"observed_at":"2026-08-10T16:57:29.303679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.468043Z","title":"Recipe for a General, Powerful, Scalable Graph Transformer","venue":null,"work_id":"0106c4e5-3c61-48d1-9116-d58955c00253","year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.306870Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:b7e654d994d5b972e12d6eaafa395403c3bcf4ffbd2726ba0a3f1469651ec9f8","observation_id":"739d60ea-51bb-4870-9cfa-519e0ac7abf4","resolution":{"observed_at":"2026-08-10T16:57:30.471991Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.309414Z","title":"Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.309414Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:a4c930b626bc51ab3ea28fa7f5522fd474382b262d35f4b283cde42ed5136f6f","observation_id":"60cee00b-5595-4a34-8aff-5ca05ecda534","resolution":{"observed_at":"2026-08-10T16:57:29.309414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.455590Z","title":"Graph-coupled oscillator networks","venue":null,"work_id":"b1b51a99-6017-4d42-ade1-c0c1a43c8a56","year":2022},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.311986Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:438f231b693412595401cb3def7b07620a64ed4b0b7846d4b5161ade172c5c7d","observation_id":"1df34719-7dee-4b8a-8539-bc468848d95f","resolution":{"observed_at":"2026-08-10T16:57:30.459184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10993","last_updated":"2023-03-20T10:21:29Z","snapshot_observed_at":"2026-08-10T02:14:11.377359Z","submitted_at":"2023-03-20T10:21:29Z","title":"A Survey on Oversmoothing in Graph Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.10993","snapshot_observed_at":"2026-08-10T16:57:29.314873Z","title":"Konstantin Rusch, Michael M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":95,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.314873Z"},"links":{"cited_paper":"/paper/2303.10993","citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:105836dc1e0c2c60df2a19698b8a82ea963f4c8c7f7b5738eb366c71ec7db50b","observation_id":"b26d4e97-75fe-49a7-9f5c-c2decba67dec","resolution":{"observed_at":"2026-08-10T16:57:29.314873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.445702Z","title":"Deep neural networks motivated by partial differential equations","venue":null,"work_id":"4da1b250-9b6e-42a2-8976-4ec1011a11dc","year":2020},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.317816Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:8d79aa23a013d54e29c51aa27fda1fb3a4c4bb6348e35d0d05c8c81715db0651","observation_id":"97752ecd-9be9-4770-8fa5-248e134cdac3","resolution":{"observed_at":"2026-08-10T16:57:30.449021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.435213Z","title":"Theoretical guarantees for permutation-equivariant quantum neural networks","venue":null,"work_id":"cf2bcb01-fe3f-4bc1-acd0-b95b38e80b57","year":2024},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.321098Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:806ac6c13f663046e980aa4bbc01e7106dd2e3b4fc67a06bbae9f5fe67327edd","observation_id":"0ab199d6-7cd5-4905-8c71-d1860caeb3cc","resolution":{"observed_at":"2026-08-10T16:57:30.439186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.325005Z","title":"Masked label prediction: Unified message passing model for semi-supervised classification","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.325005Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:8ab0aa3d697471d46dc095ed9b8fd79e05023802618371ae3f29d022e051f0f1","observation_id":"53a2f55c-e96e-424d-8a11-96f1740250f1","resolution":{"observed_at":"2026-08-10T16:57:29.325005Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:29.328424Z","title":"Rahmani, and Marzieh Aghaei","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.328424Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:7e6e91fc39c91189504fa08ce550ae76df7bf6720a0a9a214dd735ef065e2e5c","observation_id":"852e9fc8-b8ba-4276-a987-7b3fafe02f9d","resolution":{"observed_at":"2026-08-10T16:57:29.328424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:57:30.416846Z","title":"Applied nonlinear control, volume 199","venue":null,"work_id":"3b26444b-e077-4b6f-a08b-93382b2abaa1","year":1991},"citing_paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-10T16:57:29.332000Z"},"links":{"citing_paper":"/paper/2501.12732"},"observation_digest":"sha256:b12bac44a6632866374b9d0125b1ec310b68abac8216cb8073dc8681356a9bc3","observation_id":"6a721b98-86f7-4a9d-b6d4-6de6ff97d1c7","resolution":{"observed_at":"2026-08-10T16:57:30.420858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.12732","last_updated":"2025-01-22T09:09:17Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T23:35:59.528749Z","submitted_at":"2025-01-22T09:09:17Z","title":"GRAMA: Adaptive Graph Autoregressive Moving Average Models"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":78,"verified_exact":3,"verified_fuzzy":19},"total_outbound_references":127},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 100 of 127 outbound references and 0 inbound Pith citation observations for arXiv:2501.12732."}