{"as_of":"2026-08-08T01:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:25518c7d2047a13013b3f18f5d5456694822700bbff226a4f7e6d572f4029275","coverage":[{"denominator":16,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-30T23:40:06.777696Z","state":"measured"},{"denominator":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2607.26699/citation-record","integrity":"/paper/2607.26699/integrity","json":"/paper/2607.26699/citation-record.json","paper":"/paper/2607.26699"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2010.01179","last_updated":"2021-06-04T14:52:04Z","snapshot_observed_at":"2026-07-31T03:18:54.464120Z","submitted_at":"2020-10-02T19:53:05Z","title":"The Surprising Power of Graph Neural Networks with Random Node Initialization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.01179","snapshot_observed_at":"2026-07-30T23:40:06.655042Z","title":"The sur- prising power of graph neural networks with random node initialization.arXiv preprint arXiv:2010.01179,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.655042Z"},"links":{"cited_paper":"/paper/2010.01179","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:4e03be8b880abfeb0eb670a326aa2a635d3a2ace565d177d3d743fdd4d2d570d","observation_id":"fad5cb2e-c2b2-4873-958a-adb17d2b68b0","resolution":{"observed_at":"2026-07-30T23:40:06.655042Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.03199","last_updated":"2020-01-28T13:24:15Z","snapshot_observed_at":"2026-07-06T08:05:33.053199Z","submitted_at":"2019-07-06T22:26:17Z","title":"What graph neural networks cannot learn: depth vs width","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.03199","snapshot_observed_at":"2026-07-30T23:40:06.731824Z","title":"What graph neural networks cannot learn: depth vs width.arXiv preprint arXiv:1907.03199,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.731824Z"},"links":{"cited_paper":"/paper/1907.03199","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:1145ead0969619ae1e903f0fe4229b973a727c8e2349e7fdc716c69f565efad1","observation_id":"80d52464-2eca-4060-a2c3-b1cfda5af7b8","resolution":{"observed_at":"2026-07-30T23:40:06.731824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.07846","last_updated":"2020-11-12T10:30:15Z","snapshot_observed_at":"2026-07-06T09:28:53.031338Z","submitted_at":"2020-06-14T09:01:57Z","title":"Global Attention Improves Graph Networks Generalization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.07846","snapshot_observed_at":"2026-07-30T23:40:06.752628Z","title":"Global attention improves graph networks generalization.arXiv preprint arXiv:2006.07846,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.752628Z"},"links":{"cited_paper":"/paper/2006.07846","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:dd99f068fee0a1f96e56be27cf01123e36048e3ddb26f766b6a22f036b8727cc","observation_id":"f97761bf-e5e1-4ba3-a628-87d054c84277","resolution":{"observed_at":"2026-07-30T23:40:06.752628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-07-30T23:40:06.765668Z","title":"Graph attention networks.arXiv preprint arXiv:1710.10903,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.765668Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:dbe4248f9e574e5069478ae57de9a0c306a48a21167fb77e1f4d120ee9e9ce0d","observation_id":"938c4961-6e6a-4cd7-914f-7f0c4bf67468","resolution":{"observed_at":"2026-07-30T23:40:06.765668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-07-06T07:05:24.565760Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-07-30T23:40:06.777696Z","title":"How powerful are graph neural networks?arXiv preprint arXiv:1810.00826,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.777696Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:d42a9f512620126312a52ce2f6805e16881a3a8f22732ef14581675cbf664432","observation_id":"ba7cf4c4-aca5-4265-9e1b-5fd7baec3100","resolution":{"observed_at":"2026-07-30T23:40:06.777696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.06058","last_updated":"2019-12-12T16:06:47Z","snapshot_observed_at":"2026-08-01T22:04:45.268200Z","submitted_at":"2019-12-12T16:06:47Z","title":"Coloring graph neural networks for node disambiguation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.06058","snapshot_observed_at":"2026-07-30T23:40:06.694972Z","title":"Coloring graph neural networks for node disambiguation.arXiv preprint arXiv:1912.06058,","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":1989,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.694972Z"},"links":{"cited_paper":"/paper/1912.06058","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:ad21a6ce9e7a1a9f950edfe07bb442c022dbcf6873fb6dc53360dcd2c839d620","observation_id":"f9428d49-cefd-440e-a990-3990de99cdce","resolution":{"observed_at":"2026-07-30T23:40:06.694972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.01261","last_updated":"2018-10-17T17:51:36Z","snapshot_observed_at":"2026-07-06T06:42:54.610341Z","submitted_at":"2018-06-04T17:58:18Z","title":"Relational inductive biases, deep learning, and graph networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.01261","snapshot_observed_at":"2026-07-30T23:40:06.668285Z","title":"Relational inductive biases, deep learning, and graph networks.arXiv preprint arXiv:1806.01261,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":1993,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.668285Z"},"links":{"cited_paper":"/paper/1806.01261","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:b09c2125ef307d80dd510fb704dc4d3d16493bf559f8facccc041a7cd5326c75","observation_id":"51598fdb-860a-41b9-9442-9ffeec890764","resolution":{"observed_at":"2026-07-30T23:40:06.668285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.07107","last_updated":"2024-06-10T21:36:14Z","snapshot_observed_at":"2026-07-06T15:53:53.845428Z","submitted_at":"2023-07-14T01:04:18Z","title":"Graph Positional and Structural Encoder","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.07107","snapshot_observed_at":"2026-07-30T23:40:06.686596Z","title":"Graph positional and structural encoder","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.686596Z"},"links":{"cited_paper":"/paper/2307.07107","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:e94148def20180681a7282fc66d938604292d72f9f902304eba92eb078cc09e7","observation_id":"b7870e97-ceab-451e-a25b-8dbda76043fe","resolution":{"observed_at":"2026-07-30T23:40:06.686596Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.09902","last_updated":"2019-04-30T06:01:53Z","snapshot_observed_at":"2026-07-06T07:23:15.236496Z","submitted_at":"2018-12-24T11:52:27Z","title":"Invariant and Equivariant Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.09902","snapshot_observed_at":"2026-07-30T23:40:06.742866Z","title":"Invariant and equiv- ariant graph networks.arXiv preprint arXiv:1812.09902,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.742866Z"},"links":{"cited_paper":"/paper/1812.09902","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:a470e03f07a0280398c881b8ef7f948eef8c0930b9086c9cb3afe4d03ee2096c","observation_id":"dc5d4908-510e-4914-a30b-083faefa7b64","resolution":{"observed_at":"2026-07-30T23:40:06.742866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15367","last_updated":"2022-04-26T17:59:28Z","snapshot_observed_at":"2026-08-03T15:09:14.521480Z","submitted_at":"2021-11-27T02:52:10Z","title":"A Review on Graph Neural Network Methods in Financial Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.15367","snapshot_observed_at":"2026-07-30T23:40:06.770996Z","title":"Temporal-aware graph neural network for credit risk prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.770996Z"},"links":{"cited_paper":"/paper/2111.15367","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:157f2b84d262a3e10b04e2bbda47423bded62247f644012b5687aa8e673e1b79","observation_id":"fd9dc4e1-0ba7-4473-8924-c1cc0795200d","resolution":{"observed_at":"2026-07-30T23:40:06.770996Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02271","last_updated":"2024-11-12T18:11:30Z","snapshot_observed_at":"2026-08-04T08:34:16.830408Z","submitted_at":"2024-11-04T17:03:52Z","title":"On the Utilization of Unique Node Identifiers in Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02271","snapshot_observed_at":"2026-07-30T23:40:06.676120Z","title":"On the utilization of unique node identifiers in graph neural networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.676120Z"},"links":{"cited_paper":"/paper/2411.02271","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:afbc1950c0413f1098271a174d702718538ec121047dbefbe03c6176753f3c29","observation_id":"529f5821-e9e8-4981-847d-1b82695dac18","resolution":{"observed_at":"2026-07-30T23:40:06.676120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-07-06T05:10:16.862707Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-07-30T23:40:06.723577Z","title":"Semi-supervised classification with graph convolutional networks.arXiv preprint arXiv:1609.02907,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.723577Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:b0748bc72487a98da2c385e43d3f466aefcd00b33fd8d628eb32f71d5724afc5","observation_id":"be7718d9-3a0d-4e85-826b-62fdaf75d1e6","resolution":{"observed_at":"2026-07-30T23:40:06.723577Z","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":"2026-07-30T23:40:06.662166Z","title":"The logical expressiveness of graph neural networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.662166Z"},"links":{"citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:af35704d4396a6989aea32eb33d28ce3fd3f10f1883223d98e1904e22062b29b","observation_id":"4f473e5e-55c4-4e90-8cbd-39e22128f4a7","resolution":{"observed_at":"2026-07-30T23:40:06.662166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.02428","last_updated":"2019-04-25T10:06:09Z","snapshot_observed_at":"2026-08-02T18:54:43.326912Z","submitted_at":"2019-03-06T14:50:02Z","title":"Fast Graph Representation Learning with PyTorch Geometric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.02428","snapshot_observed_at":"2026-07-30T23:40:06.702900Z","title":"Fast graph representation learning with pytorch geo- metric.arXiv preprint arXiv:1903.02428,","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.702900Z"},"links":{"cited_paper":"/paper/1903.02428","citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:bf3d0db0131f8b1aef1d06d623d323ee00f847f3b292b79b98ad91088a8e89d8","observation_id":"fe554a1c-d427-4151-a318-d12451d5d681","resolution":{"observed_at":"2026-07-30T23:40:06.702900Z","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":"2026-07-30T23:40:06.759276Z","title":"Random features strengthen graph neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.759276Z"},"links":{"citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:7d43e5071fb3d2201042e122dbe36f8977cc9c32276559b777d75b87df204de4","observation_id":"b83af420-afc2-48fc-9781-4317fbb9c94a","resolution":{"observed_at":"2026-07-30T23:40:06.759276Z","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":"10.1137/24m1697402","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URLhttps://doi.org/10.1137/24M1697402","venue":"SIAM Journal on Financial Mathematics","work_id":"5cc35bf8-dcb8-4ff2-a852-5442158673cb","year":null},"citing_paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features","version":1},"reference_index":2026,"source":"pdf_text","source_observed_at":"2026-07-30T23:40:06.715465Z"},"links":{"citing_paper":"/paper/2607.26699"},"observation_digest":"sha256:c18c73f0c9c79f42f72207be591a9ea46b31df85b98f8ce7f7726b792a3cd5d9","observation_id":"b9462294-fdeb-4740-bfce-d41015bfdc68","resolution":{"observed_at":"2026-07-30T23:40:57.546583Z","resolver_source":"doi","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"}}],"paper":{"arxiv_id":"2607.26699","last_updated":"2026-07-29T09:46:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T17:20:22.308070Z","submitted_at":"2026-07-29T09:46:07Z","title":"Universality and Approximation Rates of Graph Neural Networks with Random Features"},"reference_resolution":{"displayed":16,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":16},"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 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2607.26699."}