{"as_of":"2026-08-08T02:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2bed31057b0a11b751c7e80827494979e0508a7ba6766f624b6430ef42acc688","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":20,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:32:41.888879Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T08:49:41.899171Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-07T14:32:41.888879Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2505.18728","last_updated":"2026-05-26T08:20:42Z","snapshot_observed_at":"2026-08-07T14:24:43.383023Z","submitted_at":"2025-05-24T14:53:07Z","title":"Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T14:32:41.888879Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2505.18728"},"observation_digest":"sha256:71243b59e8375ca0bd1f7a57e851f93c4960f0535ed44a4293017bb78b9e282d","observation_id":"ae70f1e7-a9e6-4238-ad1d-709bfc889cc0","resolution":{"observed_at":"2026-08-07T14:32:41.888879Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-07T11:54:11.602452Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.01231","last_updated":"2025-06-20T09:18:32Z","snapshot_observed_at":"2026-08-07T11:44:24.339705Z","submitted_at":"2025-06-02T01:03:12Z","title":"Towards Efficient Few-shot Graph Neural Architecture Search via Partitioning Gradient Contribution","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:54:11.602452Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2506.01231"},"observation_digest":"sha256:dedc1cf2bcfc7db6a3a982ed5750fa0c281bee049a34aa2259773d2c03ace664","observation_id":"1f8f8c7b-8277-415a-bc60-5bbf9a152907","resolution":{"observed_at":"2026-08-07T11:54:11.602452Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-07T00:29:33.289341Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2506.13906","last_updated":"2025-06-16T18:35:45Z","snapshot_observed_at":"2026-08-07T23:57:01.102799Z","submitted_at":"2025-06-16T18:35:45Z","title":"GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T00:29:33.289341Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2506.13906"},"observation_digest":"sha256:c0e3ad33c902779e3d58471df9116a6d978d137d65e0e0b78ae11d7c1a668302","observation_id":"a24592a4-98f9-43ad-b380-3fc4b485e629","resolution":{"observed_at":"2026-08-07T00:29:33.289341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T23:35:48.251490Z","title":"Graph neural networks exponentially lose expressive power for node classification","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.05263","last_updated":"2025-06-20T18:54:10Z","snapshot_observed_at":"2026-08-06T23:28:13.644847Z","submitted_at":"2025-06-20T18:54:10Z","title":"Rethinking Over-Smoothing in Graph Neural Networks: A Perspective from Anderson Localization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:35:48.251490Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.05263"},"observation_digest":"sha256:57ee645be06a68631b4756fe46f3d79749cc9a945eff4a9c1fd7fc66ed8f75eb","observation_id":"96892fdf-eee3-4be7-a84c-944b2e4b2dbf","resolution":{"observed_at":"2026-08-06T23:35:48.251490Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T19:36:20.539582Z","title":"when to sample","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.05533","last_updated":"2025-07-07T23:10:53Z","snapshot_observed_at":"2026-08-06T19:21:49.550460Z","submitted_at":"2025-07-07T23:10:53Z","title":"Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T19:36:20.539582Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.05533"},"observation_digest":"sha256:c97872d8d3d3c58db3ba99e39ff11448af434cd93ed4fab8fcf36b5de742a078","observation_id":"90431217-3237-4bda-8600-f6ebd509baf4","resolution":{"observed_at":"2026-08-06T19:36:20.539582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T17:46:42.030441Z","title":"Oono and T","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.10005","last_updated":"2026-06-25T23:00:47Z","snapshot_observed_at":"2026-08-06T17:39:47.236318Z","submitted_at":"2025-07-14T07:39:19Z","title":"Effects of relational graph modularity and depth on the learning performance of neural networks","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T17:46:42.030441Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.10005"},"observation_digest":"sha256:605c479b5d8558b0adc04a253c8f8f677c45bc55bcf678244950188f151bea1b","observation_id":"51e26ebb-d9e4-4a76-add2-b61e4d994463","resolution":{"observed_at":"2026-08-06T17:46:42.030441Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T17:49:10.681298Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.10626","last_updated":"2025-07-14T06:43:36Z","snapshot_observed_at":"2026-08-06T17:40:18.497064Z","submitted_at":"2025-07-14T06:43:36Z","title":"Player-Team Heterogeneous Interaction Graph Transformer for Soccer Outcome Prediction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T17:49:10.681298Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.10626"},"observation_digest":"sha256:d739abcbe83ef76e895ea72594c286779d335701c78be4d53733bcf209ab3489","observation_id":"0b75a29d-45e1-473b-bb19-8a02e2d290ed","resolution":{"observed_at":"2026-08-06T17:49:10.681298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T17:21:31.395073Z","title":"Graph neural networks exponentially lose expressive power for node classification,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2507.11077","last_updated":"2025-07-15T08:18:14Z","snapshot_observed_at":"2026-08-06T17:14:26.401052Z","submitted_at":"2025-07-15T08:18:14Z","title":"GKNet: Graph-based Keypoints Network for Monocular Pose Estimation of Non-cooperative Spacecraft","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:21:31.395073Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.11077"},"observation_digest":"sha256:2a0d532dae5cda390093bd823de632fd540631d8b9959694d7ff0b458166cb44","observation_id":"b7ac330c-8711-4187-82df-2539b58e4b35","resolution":{"observed_at":"2026-08-06T17:21:31.395073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-06T14:52:18.921283Z","title":"& Suzuki, T","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.17509","last_updated":"2025-07-23T13:47:38Z","snapshot_observed_at":"2026-08-06T14:44:19.685671Z","submitted_at":"2025-07-23T13:47:38Z","title":"Graph Neural Network Approach to Predicting Magnetization in Quasi-One-Dimensional Ising Systems","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T14:52:18.921283Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2507.17509"},"observation_digest":"sha256:85d54b6bcee9ada41c03629c38e2713970bc8c859a9ca5bf30423850bba100ab","observation_id":"4831873d-2228-4d57-b9d2-8ebfea69ace0","resolution":{"observed_at":"2026-08-06T14:52:18.921283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-05T10:25:45.791745Z","title":"Oono and T","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2509.04178","last_updated":"2025-09-04T12:53:37Z","snapshot_observed_at":"2026-08-05T10:25:44.604975Z","submitted_at":"2025-09-04T12:53:37Z","title":"Comment on \"A Note on Over-Smoothing for Graph Neural Networks\"","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T10:25:45.791745Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2509.04178"},"observation_digest":"sha256:9a03c8ede8466353edb81d77127da6d613f0d9514b3b1eca93754991e4bce06a","observation_id":"b2900aa6-760c-4025-a7fb-5c3c8dbb1e96","resolution":{"observed_at":"2026-08-05T10:25:45.791745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-04T07:54:59.999444Z","title":"Graph neural networks exponentially lose expressive power for node classification.arXiv preprint arXiv:1905.10947,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2510.23469","last_updated":"2026-06-02T15:31:54Z","snapshot_observed_at":"2026-08-06T03:58:50.690228Z","submitted_at":"2025-10-27T16:07:36Z","title":"Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-04T07:54:59.999444Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2510.23469"},"observation_digest":"sha256:5d7e40f9904d9bb226f8dd38d043a2a152521224c7e9383f0ee50e14c832997a","observation_id":"f0422122-4f74-4997-bcd0-c8a7158878da","resolution":{"observed_at":"2026-08-04T07:54:59.999444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2604.28161","last_updated":"2026-04-30T17:47:44Z","snapshot_observed_at":"2026-07-06T23:13:29.310140Z","submitted_at":"2026-04-30T17:47:44Z","title":"RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-07T06:29:03.048359Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2604.28161"},"observation_digest":"sha256:f32ed2c3b6f8cefb66c8078aacc91ef938e0fc9a52b63f4c61c4e863192beae4","observation_id":"163dde9e-e773-4dad-a7a4-84809d5fcb24","resolution":{"observed_at":"2026-05-12T10:21:30.217002Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2605.13834","last_updated":"2026-05-29T19:42:16Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:56:23Z","title":"Topology-Preserving Neural Operator Learning via Hodge Decomposition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-14T19:05:31.289091Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2605.13834"},"observation_digest":"sha256:7261651de32f8098ef9d6c05ce1d4c190d0c8ef21ff3b6bf53d68f17b9e7be06","observation_id":"3392e40c-5236-42f6-9d2e-74523992d962","resolution":{"observed_at":"2026-05-14T19:07:51.605407Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2605.13834","last_updated":"2026-05-29T19:42:16Z","snapshot_observed_at":"2026-07-06T23:25:21.032938Z","submitted_at":"2026-05-13T17:56:23Z","title":"Topology-Preserving Neural Operator Learning via Hodge Decomposition","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-06-30T21:43:50.425136Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2605.13834"},"observation_digest":"sha256:1c6fba20027ded6d5188455a3f23af8ea887d5d2782bcb2a4ff5c1b9c93a1a36","observation_id":"83d6a3cb-f12b-4358-861e-b64ce98beca8","resolution":{"observed_at":"2026-06-30T21:45:05.596078Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2605.24684","last_updated":"2026-05-23T17:42:25Z","snapshot_observed_at":"2026-08-04T12:09:05.570840Z","submitted_at":"2026-05-23T17:42:25Z","title":"Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T14:45:29.706386Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2605.24684"},"observation_digest":"sha256:4c0eb8a2577e3588353e04baf8680cb5c4cf81d7736e2c092181b5ca78d35643","observation_id":"a6f31488-bdd6-4451-8f7d-66628280dc4e","resolution":{"observed_at":"2026-06-30T14:54:45.622402Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2606.02417","last_updated":"2026-06-01T15:58:13Z","snapshot_observed_at":"2026-08-07T11:37:50.728840Z","submitted_at":"2026-06-01T15:58:13Z","title":"Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T12:35:48.607418Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2606.02417"},"observation_digest":"sha256:96c31341a4f8938ea72f6a836adaa7b5c464574193353aad740a09fdf8c85a76","observation_id":"59782fb3-b51c-460e-8228-5ed2842729a9","resolution":{"observed_at":"2026-07-02T01:06:24.785977Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2606.07327","last_updated":"2026-06-10T08:53:54Z","snapshot_observed_at":"2026-07-06T23:47:00.425715Z","submitted_at":"2026-06-05T14:45:06Z","title":"Six Open Questions in Machine-Learned Interatomic Potential Foundation Models","version":2},"reference_index":118,"source":"pdf_text","source_observed_at":"2026-06-27T21:27:50.941166Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2606.07327"},"observation_digest":"sha256:a1bb47c27586c4dccca63da1cbd3c7812055cfbafd4b08bbf2d1026890d58b28","observation_id":"74619582-170e-4ae7-9ce6-6bfd611fa032","resolution":{"observed_at":"2026-07-02T19:37:19.112966Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":"1905.10947","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-04T08:49:41.899171Z","title":"Graph Neural Networks Exponen- tially Lose Expressive Power for Node Classification","venue":null,"work_id":"364cd24e-584d-4aa4-9aa2-b43cd1f1d23c","year":1905},"citing_paper":{"arxiv_id":"2606.22429","last_updated":"2026-06-21T10:40:33Z","snapshot_observed_at":"2026-08-07T18:47:13.612767Z","submitted_at":"2026-06-21T10:40:33Z","title":"Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation","version":1},"reference_index":288,"source":"arxiv_source","source_observed_at":"2026-06-26T10:59:25.867813Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2606.22429"},"observation_digest":"sha256:6330daf69d6ff68198c3abe5175edf3b0ff069feb6406f6c7bcd2e3a6f113340","observation_id":"7446abfc-21c0-430d-9d63-41d0bd5873f8","resolution":{"observed_at":"2026-07-04T08:49:41.900645Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-07-14T00:37:04.989965Z","title":"arXiv preprint arXiv:1905.10947 , year=","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2607.10074","last_updated":"2026-07-11T01:59:00Z","snapshot_observed_at":"2026-08-06T13:32:38.188599Z","submitted_at":"2026-07-11T01:59:00Z","title":"Distance-Preserving Embeddings in Inhomogeneous Random Graphs","version":1},"reference_index":216,"source":"arxiv_source","source_observed_at":"2026-07-14T00:37:04.989965Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2607.10074"},"observation_digest":"sha256:f9785413ebe7dc38c39e541d49bd096481d75914476be1c453077e88db5cb49c","observation_id":"554bced2-68f2-4f61-b779-38982d506397","resolution":{"observed_at":"2026-07-14T00:37:04.989965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-01T22:29:30.061455Z","title":"Graph neural networks exponentially lose expressive power for node classification,","venue":null,"work_id":null,"year":1905},"citing_paper":{"arxiv_id":"2607.15773","last_updated":"2026-07-17T09:04:22Z","snapshot_observed_at":"2026-08-07T05:59:18.692446Z","submitted_at":"2026-07-17T09:04:22Z","title":"From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T22:29:30.061455Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2607.15773"},"observation_digest":"sha256:292c240a091cab4a2a0b13b7fbeaf693ba004c40ec00578eca1ec3f1917e3ac3","observation_id":"47dc7cae-6768-42ec-b7b6-0ef5b47f9dd2","resolution":{"observed_at":"2026-08-01T22:29:30.061455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1905.10947/citation-record","integrity":"/paper/1905.10947/integrity","json":"/paper/1905.10947/citation-record.json","paper":"/paper/1905.10947"},"outbound":[],"paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T07:55:41.176211Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:1905.10947."}