{"as_of":"2026-08-07T19:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b6cc2af9425bb08b3b96ff9f3dcf88adf39d5ad7999969a73a18cbdb4030de2c","coverage":[{"denominator":295,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T06:31:33.563449Z","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":"2026-08-07T06:34:17.273281+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/2607.21885/citation-record","integrity":"/paper/2607.21885/integrity","json":"/paper/2607.21885/citation-record.json","paper":"/paper/2607.21885"},"outbound":[{"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":"2026-08-01T06:31:19.346482Z","title":"A note on over-smoothing for graph neural networks, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.346482Z"},"links":{"cited_paper":"/paper/2006.13318","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:2308921c2be82522cc1c7b9b5211c86e8ad305cdb8b1c25ee0ba186a44ffef24","observation_id":"67464bd9-51e9-4640-9d03-397e515783e7","resolution":{"observed_at":"2026-08-01T06:31:19.346482Z","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":"2026-08-01T06:31:19.437323Z","title":"Grand: Graph neural diffusion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.437323Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:f2bd3c1e4c28dbcc65fec333fd1be91b2aa8bae4a93df3634d25edf65803607b","observation_id":"582652a8-4623-44a9-94ca-ed1fc941785c","resolution":{"observed_at":"2026-08-01T06:31:19.437323Z","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":"2026-08-01T06:31:19.581382Z","title":"Graph coarsening: from scientific computing to machine learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.581382Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:91586d40b4cf6b0438c9211248ce4b9b229b555563ffcb9ec6eb5b1f030f0329","observation_id":"5edb9dbd-8f6f-47b2-8e8f-a222591ccc96","resolution":{"observed_at":"2026-08-01T06:31:19.581382Z","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":"2026-08-01T06:31:19.700393Z","title":"Optimization-induced graph implicit nonlinear diffusion","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.700393Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:981891e7be27da5022788bd977e7d5a4fbd418c0ce71d1b2a448f1c02128ee58","observation_id":"334be713-b9ab-49f4-bbbe-6e57d2c90d16","resolution":{"observed_at":"2026-08-01T06:31:19.700393Z","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":"2026-08-01T06:31:19.836094Z","title":"A gromov- W asserstein geometric view of spectrum-preserving graph coarsening","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.836094Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:0ebc3692df9d6d6ac749647d39cb2edf7bc8b1f1551fcdda78b266a9df08450f","observation_id":"e5a8f95c-c1d7-4d22-a96c-9812f0ed1e90","resolution":{"observed_at":"2026-08-01T06:31:19.836094Z","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":"2026-08-01T06:31:19.980347Z","title":"Adaptive universal generalized pagerank graph neural network","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:19.980347Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:8ac878f02e60384d0c70fc5d77db48935a17f112932bb95ce47104f10ae31f5f","observation_id":"4fa63e75-5325-4ee1-8300-bcda66b7762f","resolution":{"observed_at":"2026-08-01T06:31:19.980347Z","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":"2026-08-01T06:31:20.126168Z","title":"Spectral Graph Theory , volume 92","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.126168Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:beafb97d104c6996c904eb979162d559908ea620367f4531e61147d5722f2805","observation_id":"046fc28d-096a-40ad-b437-b6070b82194d","resolution":{"observed_at":"2026-08-01T06:31:20.126168Z","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":"2026-08-01T06:31:20.306523Z","title":"Elements of information theory","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.306523Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:801d9057bf2f8312569d59320370847eb4e1fd64aafe179614d40ee45e823856","observation_id":"e3d1b60f-dbf7-4b87-8284-c12c28ac66fe","resolution":{"observed_at":"2026-08-01T06:31:20.306523Z","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":"2026-08-01T06:31:20.424611Z","title":"Re-think and re-design graph neural networks in spaces of continuous graph diffusion functionals","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.424611Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:81cd7f6c9e9f133d2aa393e121c61f66432f282e0c4d2532d822ada730588276","observation_id":"d0089d3d-4055-4022-b9cd-be9cae4e1b29","resolution":{"observed_at":"2026-08-01T06:31:20.424611Z","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":"2026-08-01T06:31:20.541826Z","title":"Halappanavar, Edoardo Serra, and Alex Pothen","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.541826Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:638cd3820a3e0a6fb5d733c07361c8a860511d2f7b416d6dc07025de5fd2b301","observation_id":"3a71176a-b558-46f5-8017-b4ea9b4e8939","resolution":{"observed_at":"2026-08-01T06:31:20.541826Z","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":"2026-08-01T06:31:20.648127Z","title":"Polynormer: Polynomial-expressive graph transformer in linear time","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.648127Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:89bcc698108f0284b60b6cac2a4580e166460c47d917a24c34ea8a688185e0e8","observation_id":"82882b04-d981-4c62-9991-8843a6078736","resolution":{"observed_at":"2026-08-01T06:31:20.648127Z","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":"2026-08-01T06:31:20.757139Z","title":"Graph coarsening via convolution matching for scalable graph neural network training","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.757139Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:62dd51ef305b840147de9003d5ad0dd014cc4cbe454aeabd9ab1a80b3d9bc11a","observation_id":"166e7ff9-c6eb-4249-b252-1b0cc9860873","resolution":{"observed_at":"2026-08-01T06:31:20.757139Z","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":"2026-08-01T06:31:20.842196Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.842196Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:8ae1635b8b837c5afe5cba248c6b20f80e1a868ffdfb4be63081bf5a7080b504","observation_id":"32b20e6e-658c-467c-b04f-7a8b0c41d25f","resolution":{"observed_at":"2026-08-01T06:31:20.842196Z","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":"2026-08-01T06:31:20.926134Z","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":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:20.926134Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:36f30aba12fd8bc59fe8fcbbaee8c4c5b5a99db5b6dd7d2ce01a041afa0537fe","observation_id":"ad82b09a-a195-4055-a2c8-4fc5525a5247","resolution":{"observed_at":"2026-08-01T06:31:20.926134Z","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":"2026-08-01T06:31:21.021591Z","title":"Faster graph embeddings via coarsening","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.021591Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:c9482e1e9a123aaba6d1e4cd427097e6a8a9803df2f0338e757da6aed0fcca80","observation_id":"80d4c959-b82d-40cf-ba1f-497f97f525b4","resolution":{"observed_at":"2026-08-01T06:31:21.021591Z","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":"2026-08-01T06:31:21.122171Z","title":"Implicit graph neural diffusion based on constrained dirichlet energy minimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.122171Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:cf651b56cb52d5f62266229d2b5f418eacf180faa90dfbdd2dab3b3fb4f43ca7","observation_id":"0e9294b1-9b0d-4735-8926-8d35a3ce5a0b","resolution":{"observed_at":"2026-08-01T06:31:21.122171Z","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":"2026-08-01T06:31:21.172855Z","title":"Bronstein","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.172855Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:4c73a59f4eb3101410541eae7fccaa58af65ff0a8eb410038bdb70a302f2e046","observation_id":"a55a8b14-6f16-45c3-acba-02acf9c0de8d","resolution":{"observed_at":"2026-08-01T06:31:21.172855Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.09769","last_updated":"2024-09-30T05:56:58Z","snapshot_observed_at":"2026-07-06T17:17:11.535092Z","submitted_at":"2024-01-18T07:36:38Z","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.09769","snapshot_observed_at":"2026-08-01T06:31:21.258312Z","title":"Learning from graphs with heterophily: Progress and future, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.258312Z"},"links":{"cited_paper":"/paper/2401.09769","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:8568662c4c88d82b1e806b50e10787f5e06db2ab954f2693bcc3c76548ed3455","observation_id":"40037a1a-69f8-4399-b7a1-ee854666abce","resolution":{"observed_at":"2026-08-01T06:31:21.258312Z","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":"2026-08-01T06:31:21.309500Z","title":"Scalable graph condensation with evolving capabilities","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.309500Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:1107745cbf7b4aee6244690c79814fc753c85b48d5c71cb4291fa72ca4a8e29d","observation_id":"a062cb5d-40f2-454c-ae0b-c14dfdc07eea","resolution":{"observed_at":"2026-08-01T06:31:21.309500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10121","last_updated":"2023-10-29T21:31:53Z","snapshot_observed_at":"2026-08-05T17:32:49.898075Z","submitted_at":"2023-10-16T06:57:24Z","title":"From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10121","snapshot_observed_at":"2026-08-01T06:31:21.387518Z","title":"From continuous dynamics to graph neural networks: Neural diffusion and beyond, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.387518Z"},"links":{"cited_paper":"/paper/2310.10121","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:0886c6bf995165531ff9d16a0d5e45389f7ebf791dccb88d8b3a0a89334c1a86","observation_id":"6684dd35-a48d-4afa-8795-7f326b31c109","resolution":{"observed_at":"2026-08-01T06:31:21.387518Z","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":"2026-08-01T06:31:21.473341Z","title":"Aditya Prakash, and Wei Jin","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.473341Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:4f79edb922d7a16fdb502ee3f4d25b5ef37f0eb239161d02aebbb5cf7e6bf860","observation_id":"f7e25632-62d6-42aa-b406-204b69475586","resolution":{"observed_at":"2026-08-01T06:31:21.473341Z","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":"2026-08-01T06:31:21.585961Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.585961Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b36941ee629bbbed696419768c684361e3b18edf43225373dc99240cce19eda4","observation_id":"d40d311a-3858-4985-a8e1-7ce5d60695ae","resolution":{"observed_at":"2026-08-01T06:31:21.585961Z","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":"2026-08-01T06:31:21.730385Z","title":"Scaling up graph neural networks via graph coarsening","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.730385Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:294f5b52077af1fd27788f99f198d5dce11c43fe5fb5b58a42ab4818ff14a04f","observation_id":"04722bd4-c9b6-49d0-8e9a-c1d065cb0f8e","resolution":{"observed_at":"2026-08-01T06:31:21.730385Z","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":"2026-08-01T06:31:21.873669Z","title":"Graph condensation for graph neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:21.873669Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:978bff289e5e63a7e1b6f0a12c1d116da2f513ab9dcc9a18f07fe910036a9e53","observation_id":"de222d8a-ecc8-4d2a-b1c2-2a825d9e2f76","resolution":{"observed_at":"2026-08-01T06:31:21.873669Z","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":"2026-08-01T06:31:22.006682Z","title":"Graph coarsening with preserved spectral properties","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.006682Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:7e7c18c01ca8ae389866233ad5c7db146fbe7eb543cfb07c44ded10102e38383","observation_id":"d5d1ee6a-aa1e-44e3-bc73-76c43043704b","resolution":{"observed_at":"2026-08-01T06:31:22.006682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.18127","last_updated":"2024-05-28T12:39:24Z","snapshot_observed_at":"2026-08-04T12:18:07.980208Z","submitted_at":"2024-05-28T12:39:24Z","title":"Graph Coarsening with Message-Passing Guarantees","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.18127","snapshot_observed_at":"2026-08-01T06:31:22.173162Z","title":"Graph coarsening with message-passing guarantees, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.173162Z"},"links":{"cited_paper":"/paper/2405.18127","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:386039f0f61efff665f4e64529816e7aee4a30b5875e80581af5a8c36765620f","observation_id":"ab6614ca-c23e-4913-8d26-86d060921636","resolution":{"observed_at":"2026-08-01T06:31:22.173162Z","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":"2026-08-01T06:31:22.353985Z","title":"Ugc: Universal graph coarsening","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.353985Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:96cb10d49efce600d0f092770fb6ab90859878c7e323783b786b7c17c2da97ed","observation_id":"a7cc6264-b47d-4b31-ad4c-8899667c3ef5","resolution":{"observed_at":"2026-08-01T06:31:22.353985Z","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":"2026-08-01T06:31:22.512965Z","title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.512965Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:a0d36962c9b1daa69a5b58d2a19e78815bcefdb4f5cc8e83d6794c29eac48347","observation_id":"bf442f8c-c9f8-4c25-a3cd-74b0b3d483f3","resolution":{"observed_at":"2026-08-01T06:31:22.512965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-01T06:31:22.625478Z","title":"Kingma and Jimmy Ba","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.625478Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:d544b219339b9829048047715d853e84932ee539b6611c56d1e6b1a86cda82fe","observation_id":"7da9128f-a5f1-4e2d-9dea-7e483d647dbe","resolution":{"observed_at":"2026-08-01T06:31:22.625478Z","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":"2026-08-01T06:31:22.717919Z","title":"Kipf and Max Welling","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.717919Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:47d793b9b6b183b1621358df14841d3695ae45952b832e4b07b995567d90c201","observation_id":"f99dbcd2-13fd-4a4e-986c-08abc8b71f8a","resolution":{"observed_at":"2026-08-01T06:31:22.717919Z","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":"2026-08-01T06:31:22.827301Z","title":"A unified framework for optimization-based graph coarsening","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.827301Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:644b4ce21ef0f5c3115c3527aacae13e11fdb18a3b92eba0f39b594b8510ae40","observation_id":"8659e7f9-13f7-4165-a7ec-8d418d3b4fb8","resolution":{"observed_at":"2026-08-01T06:31:22.827301Z","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":"2026-08-01T06:31:22.892731Z","title":"Featured graph coarsening with similarity guarantees","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:22.892731Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:26271157f84cb7caa11da7846e82c0192195d45301e18516ef1e7572b135d788","observation_id":"6e757268-dd1c-43e3-b8fa-bb3d5b3836f4","resolution":{"observed_at":"2026-08-01T06:31:22.892731Z","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":"2026-08-01T06:31:23.081211Z","title":"Finding global homophily in graph neural networks when meeting heterophily","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.081211Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:e1fb75c6638d4d6b53beb838fb5b2564a3006db6905bea1d7719e8216c7693a2","observation_id":"344039d4-881f-4a4d-8fa3-4ff6257f48de","resolution":{"observed_at":"2026-08-01T06:31:23.081211Z","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":"2026-08-01T06:31:23.245812Z","title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.245812Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b4f2fce92c90383b9f696be5f78bbbc85e5d925ef23ca87e427eee2043f2c2bf","observation_id":"f93c2413-4f80-4651-a728-66a34bc2ec13","resolution":{"observed_at":"2026-08-01T06:31:23.245812Z","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":"2026-08-01T06:31:23.335152Z","title":"Graph summarization methods and applications: A survey","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.335152Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:99f2caa48cdb2f211cc82175c8a11fad4b4d315fc836f357c7064d34fa23aeaf","observation_id":"fc06e17d-5fb1-4eb2-9b3e-6263a2c917c5","resolution":{"observed_at":"2026-08-01T06:31:23.335152Z","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":"2026-08-01T06:31:23.517521Z","title":"Spectrally approximating large graphs with smaller graphs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.517521Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:35fa096c8d44ee29874d92e951dfa1d6e16b242d199372aaf137b1060fadaf4a","observation_id":"0fe0f442-4beb-446f-94d9-868e1516312b","resolution":{"observed_at":"2026-08-01T06:31:23.517521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-01T06:31:23.675195Z","title":"Li, Jian Tang, Guy Wolf, and Stefanie Jegelka","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.675195Z"},"links":{"cited_paper":"/paper/2407.09618","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:5f6bea5a961cfc3dd7b0d781708facdfa5942fbffc41fdac3469a31185ed316e","observation_id":"deb85a26-1d57-4d9d-af22-76e3c5edecee","resolution":{"observed_at":"2026-08-01T06:31:23.675195Z","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":"2026-08-01T06:31:23.820167Z","title":"A fractional graph laplacian approach to oversmoothing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.820167Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b0e3ca043537470a4f133531ca26fe987b9a9ff97e7239ecba409578de1a877c","observation_id":"77e1801b-dc0a-4e8e-94f2-b5c60692d3ee","resolution":{"observed_at":"2026-08-01T06:31:23.820167Z","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":"2026-08-01T06:31:23.895980Z","title":null,"venue":null,"work_id":null,"year":1964},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:23.895980Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:bc3897b0d8382210ad99bc559225a04498e340fd8081d220f3fbd006cee737a0","observation_id":"632485e8-88e6-442c-9c76-f4191143df11","resolution":{"observed_at":"2026-08-01T06:31:23.895980Z","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":"2026-08-01T06:31:24.021539Z","title":"Perona and J","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.021539Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:da46f609d4cfdccf20a015645d1b63bd83d36d46893187e6837f41afcb4fe508","observation_id":"4a52299e-da00-4a0a-99e9-be083b865da8","resolution":{"observed_at":"2026-08-01T06:31:24.021539Z","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":"2026-08-01T06:31:24.127720Z","title":"A critical look at the evaluation of GNN s under heterophily: Are we really making progress? In International Conference on Learning Representations, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.127720Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:0cbb1bc18d11ffc2880536d12d3e08a47aa89897ac309a895dd5d5fe4a50a010","observation_id":"3a8be85f-3511-4437-8a3c-f5c216c3b7d5","resolution":{"observed_at":"2026-08-01T06:31:24.127720Z","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":"2026-08-01T06:31:24.241847Z","title":"Aditya Prakash, Chanhyun Kang, Yao Zhang, and V.S","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.241847Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:dfd8fdc2b50063f27db92672264471b18a8403f6906ca504bad40f070768ef3f","observation_id":"f75a0101-f497-487f-b0d1-f54bd896e142","resolution":{"observed_at":"2026-08-01T06:31:24.241847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.10688","last_updated":"2020-09-17T04:47:27Z","snapshot_observed_at":"2026-08-05T23:14:13.965992Z","submitted_at":"2019-11-25T03:44:34Z","title":"Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.10688","snapshot_observed_at":"2026-08-01T06:31:24.361104Z","title":"Rethinking softmax with cross-entropy: Neural network classifier as mutual information estimator, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.361104Z"},"links":{"cited_paper":"/paper/1911.10688","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:4de484aaf8763c3b92f67ca138f4f35152bb57ea8433b676d27f76c36848ac79","observation_id":"b0b3f441-0862-4ecd-8bb5-78a5837a2386","resolution":{"observed_at":"2026-08-01T06:31:24.361104Z","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":"2026-08-01T06:31:24.541210Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.541210Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:f99c58cacdb3596355e868ddad8d2ee91801b155a5450111bcde15ccae2fb344","observation_id":"1fed0015-8486-462c-bea1-9f724dbc0f78","resolution":{"observed_at":"2026-08-01T06:31:24.541210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.10993","last_updated":"2023-03-20T10:21:29Z","snapshot_observed_at":"2026-07-06T15:05:38.707313Z","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":"2026-08-01T06:31:24.661666Z","title":"Konstantin Rusch, Michael M","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.661666Z"},"links":{"cited_paper":"/paper/2303.10993","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:ae6a630efa4a924b5d665c2d07bf96958589d07f80931ceaad3333a2c7baaf8f","observation_id":"d6623d33-4c8f-40e0-98c9-5e572d3b28a1","resolution":{"observed_at":"2026-08-01T06:31:24.661666Z","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":"2026-08-01T06:31:24.836278Z","title":"Scalable graph neural network training: The case for sampling","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.836278Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:83a5fb16690d2ba755625348baa951b3878e1a95a06b8ae5a81445dc70381dd1","observation_id":"6d8edef5-4a4d-4e11-9bc6-bbb163121961","resolution":{"observed_at":"2026-08-01T06:31:24.836278Z","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":"2026-08-01T06:31:24.995480Z","title":"Graph attention networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:24.995480Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:6911d1918dd9b69d2a910384f8e4bfaa12d13b010753e3a53aa57d24376d07ce","observation_id":"9f7172e7-ec40-43db-8458-865621964cba","resolution":{"observed_at":"2026-08-01T06:31:24.995480Z","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":"2026-08-01T06:31:25.149245Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.149245Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:abb7fa4647fbd007cd4d472839c2a3ad29127c8b18e9e039c63fbf1add7fc11f","observation_id":"0b4fd53c-9cb7-4b0c-a803-3085160da7e8","resolution":{"observed_at":"2026-08-01T06:31:25.149245Z","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":"2026-08-01T06:31:25.372674Z","title":"Sgformer: Simplifying and empowering transformers for large-graph representations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.372674Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:ac01213a667a651f12a45b8f309133999b44ef84ec928f9a3d31cb652ab7a3c4","observation_id":"53dacdfa-98dd-43d9-8e7f-8a7888029b8d","resolution":{"observed_at":"2026-08-01T06:31:25.372674Z","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":"10.1609/aaai.v39i12.33404","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Graph coarsening via supervised granular-ball for scalable graph neural network training","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"4193a46b-ef2f-43a7-809a-a57dc24f364b","year":2025},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.500993Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:4e3ba6055ef34b2a9895afc204999058e79f5a0b8d5feb0a19185c5a70a34044","observation_id":"3789f2ae-9a62-45ec-b4f6-553de3b5cfe5","resolution":{"observed_at":"2026-08-01T06:33:25.541460Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T06:31:25.630408Z","title":"Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.630408Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:63a11fdaab0caf7c2a4f14fa242072f74a4ca81559a47b4ffb30ffd32c04e516","observation_id":"7df88939-b30f-40f0-8c31-a1dd24b9bafd","resolution":{"observed_at":"2026-08-01T06:31:25.630408Z","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":"2026-08-01T06:31:25.755811Z","title":"A survey on multi-task learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.755811Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:a834180e4958a942f9a5b37c1ad56ec7aa2e508329c2978896d8392487d8dc4c","observation_id":"7b20814a-8545-4b30-ae75-7a232fee9c68","resolution":{"observed_at":"2026-08-01T06:31:25.755811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07082","last_updated":"2026-04-18T08:18:16Z","snapshot_observed_at":"2026-08-02T07:00:41.767213Z","submitted_at":"2022-02-14T23:07:47Z","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.07082","snapshot_observed_at":"2026-08-01T06:31:25.883847Z","title":"Yu, and Shirui Pan","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:25.883847Z"},"links":{"cited_paper":"/paper/2202.07082","citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:6f94b1360f69f4a80081b2bded92958b0ca839569171b208f3a1cefa4f229b80","observation_id":"dde2a014-b07f-4290-9f23-34ff7b11c136","resolution":{"observed_at":"2026-08-01T06:31:25.883847Z","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":"2026-08-01T06:31:26.185361Z","title":"Beyond homophily in graph neural networks: Current limitations and effective designs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:26.185361Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:ce833e99ff15581b83765236b3679ab8d20219ed56786ced9ba85b0e8b8993be","observation_id":"305a52d9-c06a-47d7-bfc7-aba8b4a9ce15","resolution":{"observed_at":"2026-08-01T06:31:26.185361Z","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":"2026-08-01T06:31:26.499112Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , author=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:26.499112Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:339b86d45a23ce81de976d72f5d2145f09a748988df6b9ab1266f60022fae60a","observation_id":"31dfa6c5-ecb9-4ba3-b78f-4c4add77c391","resolution":{"observed_at":"2026-08-01T06:31:26.499112Z","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":"2026-08-01T06:31:26.626262Z","title":"2020 , isbn =","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:26.626262Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:5339897d11608664d1fa4008dff4ec0dec5e98b9d5a19d11858917348e9dc17d","observation_id":"8becf0ab-5c5f-4a32-a0bf-04fe07574246","resolution":{"observed_at":"2026-08-01T06:31:26.626262Z","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":"2026-08-01T06:31:26.773859Z","title":"2021 , eprint=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:26.773859Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:c381584facb3006a18b2c50fe6d2a1fd7e85bf3b90764f6408bd2e0dd915cfeb","observation_id":"1f587add-8332-4cd2-810f-f643638c7828","resolution":{"observed_at":"2026-08-01T06:31:26.773859Z","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":"2026-08-01T06:31:26.915845Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:26.915845Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:1b40f42c552550de9f3bb8c29ceb9d0ea1bf9ee4d2beccc070f8659f3709270c","observation_id":"267dc409-d19d-4c77-bece-5c6b16ff83bb","resolution":{"observed_at":"2026-08-01T06:31:26.915845Z","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":"2026-08-01T06:31:27.075070Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.075070Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b99005da762352554769884a6c91825a7c54cd78695422b3aa5e2db8b81bb545","observation_id":"a40eb9f9-7692-48a0-841d-75d280ac8be3","resolution":{"observed_at":"2026-08-01T06:31:27.075070Z","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":"2026-08-01T06:31:27.221537Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.221537Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:c0d15d62341a09d5c85184222863f6062d4004983e237ca0fc9823b84804df80","observation_id":"bd300021-5858-4bbd-ae88-a8fe87361029","resolution":{"observed_at":"2026-08-01T06:31:27.221537Z","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":"2026-08-01T06:31:27.311029Z","title":"ICML , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.311029Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:0821ea67d01a2b1a4594bef22048b77145159c1861f08dd8772f0621c746ed25","observation_id":"706f4f39-94b7-475d-b11a-f721738bbc95","resolution":{"observed_at":"2026-08-01T06:31:27.311029Z","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":"2026-08-01T06:31:27.414143Z","title":"Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) , publisher=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.414143Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:ec00601bdb342adcef038318546dfc34d48887a8241fc2e91b770c1c3bc87fd6","observation_id":"aea32836-c49c-48f5-a9fa-bf4996653e63","resolution":{"observed_at":"2026-08-01T06:31:27.414143Z","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":"10.1609/aaai.v38i13.29326","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , author=","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"150f6a9f-b271-4fff-a29a-4628ef564a94","year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.552054Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b12269f82ac4ee3b68f73a3e1f222dead58ee608de8de1e91d6a37e254b7d459","observation_id":"49c1e09b-32a8-4855-83b8-70a602bd5323","resolution":{"observed_at":"2026-08-01T06:33:24.974627Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v38i10.29058","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the AAAI Conference on Artificial Intelligence , author=","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"eaaed57b-83e0-4ac9-9181-79152d30679c","year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.735419Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:78069c9b0cd95498efa9b9625f8e07c6027e6620de088c0b91c5c3c6543b43bc","observation_id":"cb42962e-94bf-4d6c-804c-450ebb234388","resolution":{"observed_at":"2026-08-01T06:33:24.790108Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T06:31:27.898559Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:27.898559Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:48a645a439bbbbcd19e1ff5cfed673b099277b591cf9e87e9756e78e201f07c8","observation_id":"2da3f2fe-5ab2-4481-ab02-56acaa1f707a","resolution":{"observed_at":"2026-08-01T06:31:27.898559Z","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":"10.1609/aaai.v38i12.29246","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering , url=","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","work_id":"b28653e8-3c04-4006-8644-f77a6c9173ab","year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":69,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.027414Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:dd2c4799009b2e38a07365e3b223e1cf4927ebd257fe731b719c29bc5e17b200","observation_id":"ad1ad098-1e7b-4447-9a78-bb79b8dbafba","resolution":{"observed_at":"2026-08-01T06:33:24.641681Z","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T06:31:28.190945Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.190945Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:12a691440278677b8f1c555006cd29e1641188274d4fee3bf5f26033cea1c705","observation_id":"d73f1588-61b4-46bd-94eb-745a84b58a92","resolution":{"observed_at":"2026-08-01T06:31:28.190945Z","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":"2026-08-01T06:31:28.336498Z","title":"2023 , eprint=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":71,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.336498Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:9fcda071c0d28adda424311ad838989e4c6a2166bbf759f7fbb10f99eed11f81","observation_id":"8ed8b308-99e1-4853-90fc-73a6cbc99a8b","resolution":{"observed_at":"2026-08-01T06:31:28.336498Z","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":"2026-08-01T06:31:28.477057Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":72,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.477057Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:69d2883f573ed95b1fdd0171d858b15738daeea8ff635add320f8930c86715fd","observation_id":"1a74f6c6-d064-4a53-bc12-44bd492ba124","resolution":{"observed_at":"2026-08-01T06:31:28.477057Z","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":"2026-08-01T06:31:28.648347Z","title":"2017 , eprint=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.648347Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:650a5db90d9c661a678bf095b515788776156cf68a5bd70015ef37cf565ee5c4","observation_id":"7d662b35-2e05-4977-b45e-cebe148803ff","resolution":{"observed_at":"2026-08-01T06:31:28.648347Z","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":"2026-08-01T06:31:28.760688Z","title":"and Bresson, Xavier , title=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.760688Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:99ee9dffb4d9a5df7153498bccb8d1886b44e0e064583efd275811e0589437df","observation_id":"070b162c-b0a1-444f-8b23-133c357362a7","resolution":{"observed_at":"2026-08-01T06:31:28.760688Z","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":"2026-08-01T06:31:28.924843Z","title":"Factorization Machines , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":75,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:28.924843Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:a67806fa7b43a2fe7ba889b3219f959ea67e3a89d7418d5a64f193a3acd842ae","observation_id":"72d785fa-48c6-4c2a-9b74-9062391f1858","resolution":{"observed_at":"2026-08-01T06:31:28.924843Z","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":"2026-08-01T06:31:29.105986Z","title":"Higher-Order Factorization Machines , url=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":76,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.105986Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:479e371c0168f86357711309ddcbb4065cc6ffb225ef107923dbb9eebc3c2e06","observation_id":"f5bed093-a8aa-495b-99d0-a1853f4e73a1","resolution":{"observed_at":"2026-08-01T06:31:29.105986Z","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":"2026-08-01T06:31:29.242049Z","title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence , pages=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":77,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.242049Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:d816b3ad78ad083d77b1984b3b52151133aab62d46db9a647775f6b2e110a03a","observation_id":"50d462ec-6b22-4a28-ad7f-a2c5a38d0267","resolution":{"observed_at":"2026-08-01T06:31:29.242049Z","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":"2026-08-01T06:31:29.408087Z","title":"2017 , eprint=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":78,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.408087Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:a5cfe8c39c455e64e18bd7db32a777e816e4ef012bdd2790a46a68474676216a","observation_id":"f22be240-8182-466f-9ab2-db27cc1e3aa0","resolution":{"observed_at":"2026-08-01T06:31:29.408087Z","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":"2026-08-01T06:31:29.554013Z","title":"and Sturmfels, Pascal and Lee, Su-In , title=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.554013Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:5cf6d409f08ea02df839f15613617259c93148807a8964ea38770beb22c3d260","observation_id":"93770403-073c-4bb4-80b1-fc968082ad96","resolution":{"observed_at":"2026-08-01T06:31:29.554013Z","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":"2026-08-01T06:31:29.689685Z","title":"2020 , isbn=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.689685Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:301f0ecf4b23ce4eed4c994a6826c0b5620e87b6acebc42d0a7787f7fb4bf995","observation_id":"e99f2fd9-6b72-444b-b7db-749a4de55be9","resolution":{"observed_at":"2026-08-01T06:31:29.689685Z","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":"2026-08-01T06:31:29.862356Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":81,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:29.862356Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:dba7de59db5baaeb43f10086b8c0aee0b95e9aafb8b7faaafb389a57281a05ec","observation_id":"36f787c9-64ef-4f14-b0ed-34fd6a054084","resolution":{"observed_at":"2026-08-01T06:31:29.862356Z","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":"2026-08-01T06:31:30.030185Z","title":"Aggarwal and Dawei Yin and Jiliang Tang , title=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:30.030185Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:b0c9d8e1238b57e80bb9fe4b1310a7f85bb62ea4959126f000b04fa8a55734f5","observation_id":"f43daee2-fa99-48c9-a246-84da46a89441","resolution":{"observed_at":"2026-08-01T06:31:30.030185Z","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":"2026-08-01T06:31:30.206306Z","title":"The Eleventh International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:30.206306Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:9dc16195f678fcfc5d11f966bf68e45eb7734e2219d7cbd72d5f1fc2b0730bf1","observation_id":"76641a9b-6af9-49c2-be7c-e57ff60299ac","resolution":{"observed_at":"2026-08-01T06:31:30.206306Z","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":"2026-08-01T06:31:30.375998Z","title":"Dual Feature Interaction-Based Graph Convolutional Network , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:30.375998Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:70af5cd11e22cebdef51d605b86fa2d058f4e936669c35b830ad495c7d4b1fd5","observation_id":"f4f76a50-13a2-48fd-955b-328f37d7355d","resolution":{"observed_at":"2026-08-01T06:31:30.375998Z","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":"2026-08-01T06:31:30.525711Z","title":"2021 , isbn=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":85,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:30.525711Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:c04950824c978f95318ce6749a8dd771e3394d7fb7401e0dc1ec26bbe5e49278","observation_id":"151f48d5-0eef-4694-814b-a188af90f152","resolution":{"observed_at":"2026-08-01T06:31:30.525711Z","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":"2026-08-01T06:31:30.911207Z","title":"EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks , url=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:30.911207Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:508204da6a29a764bfaf6ec0a9d8fd167caa21776a2b53c7196021577a90c5a0","observation_id":"2a5aff45-0612-47b1-bfa6-16af1cfa5147","resolution":{"observed_at":"2026-08-01T06:31:30.911207Z","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":"2026-08-01T06:31:31.104495Z","title":"2024 , eprint=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":88,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.104495Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:98c668e265bbea15064e30086263a08a5dafc7cef859e0cbd59a3b2096b4e8b4","observation_id":"5f92ea83-89b5-4c80-abd5-047bfec009c9","resolution":{"observed_at":"2026-08-01T06:31:31.104495Z","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":"2026-08-01T06:31:31.302979Z","title":"Node-Oriented Spectral Filtering for Graph Neural Networks , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":89,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.302979Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:d287991dd6cc35aca5fc837649fa2cafdf5a944dedb3203a9aaff3726f0b5067","observation_id":"e97e7949-547d-460d-b8ba-0f73b28e2005","resolution":{"observed_at":"2026-08-01T06:31:31.302979Z","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":"2026-08-01T06:31:31.472833Z","title":"Graph Neural Networks With Convolutional ARMA Filters , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":90,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.472833Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:5baae928b68683ad1c2190caadcb3a5ad9d90de6a526eba296ed05d1c0b5d8f5","observation_id":"544a3b58-2f69-4c5d-b3cc-bef92747cad8","resolution":{"observed_at":"2026-08-01T06:31:31.472833Z","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":"2026-08-01T06:31:31.633068Z","title":", journal=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":91,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.633068Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:1b26b9fa06fed09aabac754801d25176e94646b4eacac83c540772ea7f30603d","observation_id":"248c0338-7595-468d-b721-85178027ef6d","resolution":{"observed_at":"2026-08-01T06:31:31.633068Z","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":"2026-08-01T06:31:31.802822Z","title":"Rational Neural Networks for Approximating Graph Convolution Operator on Jump Discontinuities , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":92,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.802822Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:d0c22f160eff40bf698e53e01ddd489f1c60e49a46960d9184f41b8f8ff710c2","observation_id":"a146b25d-3e6b-4c35-85fc-0ea32fc05525","resolution":{"observed_at":"2026-08-01T06:31:31.802822Z","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":"2026-08-01T06:31:31.945850Z","title":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , month =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":93,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:31.945850Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:e805ad34cc7d7e899d5a648952eef6e07fbfb9652e98ab02a8f5668eabf62f2d","observation_id":"5d4b5742-5dcf-489b-8fe2-774b993c217e","resolution":{"observed_at":"2026-08-01T06:31:31.945850Z","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":"2026-08-01T06:31:32.063129Z","title":"2022 , editor=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.063129Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:9a91fc82aa48405df61e968c7cfbfcd1b8881b2f21b767444a929dac2a824ddd","observation_id":"ddd917a9-4621-4ffa-b086-9161e971985a","resolution":{"observed_at":"2026-08-01T06:31:32.063129Z","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":"2026-08-01T06:31:32.385987Z","title":"and Fefferman, Robert , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":96,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.385987Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:22aa5d14a3d8d16ecfbf6858aca3c54d74048c7dfa840d2de5be8e92526ae751","observation_id":"bbe06900-e544-40c5-a8ba-dd5256f2e917","resolution":{"observed_at":"2026-08-01T06:31:32.385987Z","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":"2026-08-01T06:31:32.537391Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.537391Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:2a806fd8875edc4493ee4088d25487ac9ba0fb6c80c6389f23079e73ee4cfb43","observation_id":"32ff9d52-b638-478a-ba6d-be7448484cbd","resolution":{"observed_at":"2026-08-01T06:31:32.537391Z","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":"2026-08-01T06:31:32.649962Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":98,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.649962Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:749e5f8fa74bc06336b67e1ca4247bd846528fea9a0a5f6c05a1bb75a7cd47e2","observation_id":"326c4ca5-8846-4ac8-a378-a214cd9e423c","resolution":{"observed_at":"2026-08-01T06:31:32.649962Z","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":"2026-08-01T06:31:32.776344Z","title":"Infinite Impulse Response Graph Filters in Wireless Sensor Networks , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":99,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.776344Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:568ee70c715fd645ad19e6677b0e20cf2e9b5872ab9ce34b97faca7cf6b75839","observation_id":"945335af-dee9-43f0-a15d-2502f90e0671","resolution":{"observed_at":"2026-08-01T06:31:32.776344Z","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":"2026-08-01T06:31:32.906956Z","title":"Autoregressive Moving Average Graph Filtering , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":100,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:32.906956Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:1f5d8bf19335a54a376373a73a17f0f74846f0090a144c9296ad334b1998c970","observation_id":"f96e4836-a841-4cc8-b5fd-0f3e49a0c27d","resolution":{"observed_at":"2026-08-01T06:31:32.906956Z","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":"2026-08-01T06:31:33.010731Z","title":"Rational Chebyshev Graph Filters , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:33.010731Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:d737fb08ec2f0ce8b632e40e823c08c1324799262e482c1ec5e1909a66a05c3f","observation_id":"b15e8b44-e2ea-420b-be02-7bba6518be5c","resolution":{"observed_at":"2026-08-01T06:31:33.010731Z","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":"2026-08-01T06:31:33.075073Z","title":"Fourier-Based and Rational Graph Filters for Spectral Processing , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":102,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:33.075073Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:cdeaf5a151e0d784fc3352e94055e2e59a0247b665665a4bc3f9df602e330e7f","observation_id":"032aac9d-cfbb-435f-af77-259260eada70","resolution":{"observed_at":"2026-08-01T06:31:33.075073Z","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":"2026-08-01T06:31:33.238539Z","title":"Optimal surface smoothing as filter design","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":103,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:33.238539Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:3ddfe3505cd69503486ab38284c6565265a2930983a8283e5e6bf06986694b10","observation_id":"8a4c5b2e-20c9-454a-ad4a-0523bcd71259","resolution":{"observed_at":"2026-08-01T06:31:33.238539Z","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":"2026-08-01T06:31:33.401987Z","title":", booktitle=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":104,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:33.401987Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:7f763d8a21dbac940d79ec6b5a6e7f309f32c600755108491646d1ff8a1aa0c9","observation_id":"92fced29-8b55-4d5d-a08b-8b726b52ebf8","resolution":{"observed_at":"2026-08-01T06:31:33.401987Z","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":"2026-08-01T06:31:33.563449Z","title":"and Zhi-Quan Luo and Sturm, J.F","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement","version":1},"reference_index":105,"source":"arxiv_source","source_observed_at":"2026-08-01T06:31:33.563449Z"},"links":{"citing_paper":"/paper/2607.21885"},"observation_digest":"sha256:6e92b7d4e6f44bc4e686f14a8f2db5819b483a489b6e768dc2841e213c2714e4","observation_id":"d921d4ec-0bff-4b94-8abd-6c367047ac62","resolution":{"observed_at":"2026-08-01T06:31:33.563449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.21885","last_updated":"2026-07-24T01:29:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T15:31:19.115152Z","submitted_at":"2026-07-24T01:29:28Z","title":"Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":96,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":295},"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 7 August 2026, this Paper Citation Record lists 100 of 295 outbound references and 0 inbound Pith citation observations for arXiv:2607.21885."}