{"as_of":"2026-08-09T05:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:22f9deb7f4f4a10e3445486cc927d0aaec4013381676615bee61577c6b6af867","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":28,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:55:23.669030Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T20:08:56.258128Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T11:55:23.669030Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00991","last_updated":"2025-08-05T09:17:28Z","snapshot_observed_at":"2026-08-07T22:16:51.729179Z","submitted_at":"2025-06-01T12:46:14Z","title":"GOBench: Benchmarking Geometric Optics Generation and Understanding of MLLMs","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:55:23.669030Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.00991"},"observation_digest":"sha256:dba1e7b177981cba4441d28036adda9d36071bdda21da7ee11c58c389bc6673a","observation_id":"5601e2a7-2844-46bf-9e01-bda663249c7b","resolution":{"observed_at":"2026-08-07T11:55:23.669030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T06:04:14.300110Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.06157","last_updated":"2025-07-30T03:58:06Z","snapshot_observed_at":"2026-08-07T05:57:11.148750Z","submitted_at":"2025-06-06T15:21:24Z","title":"Masked Language Models are Good Heterogeneous Graph Generalizers","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T06:04:14.300110Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.06157"},"observation_digest":"sha256:bbe9ff40d68ecd37d59367f1cec57b547ecd4f82dfef7c55b6ebed3b9e616921","observation_id":"0a058a99-bfd4-4e91-89ec-244273713cb2","resolution":{"observed_at":"2026-08-07T06:04:14.300110Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T05:44:53.242558Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07168","last_updated":"2025-06-08T14:34:29Z","snapshot_observed_at":"2026-08-07T22:51:47.659500Z","submitted_at":"2025-06-08T14:34:29Z","title":"Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:44:53.242558Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.07168"},"observation_digest":"sha256:218d959cfdebe1deb419e878ae82e659dd0fc33979120da8bbe2afebe621ecf0","observation_id":"dd0c25b5-6daa-44e1-87ea-7fba408ee48a","resolution":{"observed_at":"2026-08-07T05:44:53.242558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T05:21:34.973120Z","title":"One for all: Towards training one graph model for all classification tasks.arXiv preprint arXiv:2310.00149, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08298","last_updated":"2025-06-15T02:48:38Z","snapshot_observed_at":"2026-08-07T05:11:40.227366Z","submitted_at":"2025-06-10T00:03:56Z","title":"H$^2$GFM: Towards unifying Homogeneity and Heterogeneity on Text-Attributed Graphs","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:21:34.973120Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.08298"},"observation_digest":"sha256:72539d08c6d0ffecd1814df78fb7f977d1a7c8d6c8f066d546db0cce6925fb5b","observation_id":"ad94d738-870e-4d21-9b8b-c54b44345d04","resolution":{"observed_at":"2026-08-07T05:21:34.973120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T00:55:54.768518Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.12468","last_updated":"2025-06-17T03:17:11Z","snapshot_observed_at":"2026-08-07T00:46:40.868394Z","submitted_at":"2025-06-14T12:14:15Z","title":"Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:54.768518Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.12468"},"observation_digest":"sha256:44d1dce36534516fb46371af6dfa7cfa16f6ea619e82ae79d5ed8638ded90991","observation_id":"549d0ebe-17a6-4db1-a783-771c445e2e07","resolution":{"observed_at":"2026-08-07T00:55:54.768518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-07T04:43:40.606673Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.21559","last_updated":"2025-06-11T16:38:01Z","snapshot_observed_at":"2026-08-08T14:59:08.856572Z","submitted_at":"2025-06-11T16:38:01Z","title":"GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:43:40.606673Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.21559"},"observation_digest":"sha256:0de033ea585eb61294ca200365ac54bf0c5fa83992570cbea78aaefded005f7c","observation_id":"7e4da553-463a-44eb-99d1-de1195529d05","resolution":{"observed_at":"2026-08-07T04:43:40.606673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-06T22:42:24.882715Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.22510","last_updated":"2025-06-26T03:14:50Z","snapshot_observed_at":"2026-08-08T15:27:25.382897Z","submitted_at":"2025-06-26T03:14:50Z","title":"Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:42:24.882715Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2506.22510"},"observation_digest":"sha256:8d3879d874ff0e97544322ccb5d940f5cc6dc4ca4502c11515ff16921269fdf8","observation_id":"c6dbf751-68f6-4729-a142-ba296dee624d","resolution":{"observed_at":"2026-08-06T22:42:24.882715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-06T17:37:11.713264Z","title":"One for all: Towards training one graph model for all classification tasks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.10539","last_updated":"2025-07-14T17:57:45Z","snapshot_observed_at":"2026-08-08T20:37:18.280461Z","submitted_at":"2025-07-14T17:57:45Z","title":"Graph World Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:37:11.713264Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2507.10539"},"observation_digest":"sha256:86068ddd1a7f732e29d337c7fb3f7a9d82cd64a8fcbcc243c4b98a88f654a0b3","observation_id":"c0db666d-0e3c-4450-8925-73551a9f76d9","resolution":{"observed_at":"2026-08-06T17:37:11.713264Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-06T15:11:16.078316Z","title":"One for all: Towards training one graph model for all classification tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.16541","last_updated":"2025-07-22T12:49:24Z","snapshot_observed_at":"2026-08-07T22:03:21.200055Z","submitted_at":"2025-07-22T12:49:24Z","title":"A Comprehensive Data-centric Overview of Federated Graph Learning","version":1},"reference_index":193,"source":"pdf_text","source_observed_at":"2026-08-06T15:11:16.078316Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2507.16541"},"observation_digest":"sha256:d364afe512721149ddb2d3a21d7f5f7b38d76bbb43f0580f2f2141a10b54f1f7","observation_id":"42fe4888-432c-43de-a2d8-1c6e730b61c8","resolution":{"observed_at":"2026-08-06T15:11:16.078316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-05T14:36:36.408242Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.06975","last_updated":"2025-08-28T19:13:10Z","snapshot_observed_at":"2026-08-07T21:39:28.054869Z","submitted_at":"2025-08-28T19:13:10Z","title":"GSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T14:36:36.408242Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2509.06975"},"observation_digest":"sha256:6a75b65100d7aba75ca0569e2f25f62e90630f55fe156316545a12bd27066b27","observation_id":"e9c0a918-ce5c-4d5b-87ac-b462cd8191ce","resolution":{"observed_at":"2026-08-05T14:36:36.408242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-04T14:42:34.054032Z","title":"arterial","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.24256","last_updated":"2026-07-13T05:58:29Z","snapshot_observed_at":"2026-08-07T23:51:39.339618Z","submitted_at":"2025-09-29T04:05:48Z","title":"Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T14:42:34.054032Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2509.24256"},"observation_digest":"sha256:22948af06e27f65f938f4899388ee5485ecd646c43d3a9183bf286b755c9d2aa","observation_id":"fb698b0a-cccf-45f5-ade5-2bdf5e810787","resolution":{"observed_at":"2026-08-04T14:42:34.054032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2602.04244","last_updated":"2026-05-07T12:31:58Z","snapshot_observed_at":"2026-07-06T22:44:28.488131Z","submitted_at":"2026-02-04T06:06:28Z","title":"GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T07:25:35.324582Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2602.04244"},"observation_digest":"sha256:4f2fda51663e5058ac89526d37864b5977eb6134d59b23947863cc65a389e1f2","observation_id":"4119f2d0-c271-4800-9443-55b8e7126ad2","resolution":{"observed_at":"2026-05-16T07:27:31.606330Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-03T00:07:40.705713Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2602.11641","last_updated":"2026-05-31T02:32:04Z","snapshot_observed_at":"2026-08-09T02:42:12.079273Z","submitted_at":"2026-02-12T06:53:35Z","title":"Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T00:07:40.705713Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2602.11641"},"observation_digest":"sha256:9dc6e7b98ce7607fbd7dbe15e7287eb576b1bec3c795cc6b503caa71358015e9","observation_id":"21481ebf-a9bc-4c16-b730-2c3f197238f0","resolution":{"observed_at":"2026-08-03T00:07:40.705713Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2603.02938","last_updated":"2026-05-21T07:56:15Z","snapshot_observed_at":"2026-07-06T22:47:41.997192Z","submitted_at":"2026-03-03T12:47:44Z","title":"Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-22T10:30:01.910920Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2603.02938"},"observation_digest":"sha256:67c637672af093742bab14dd501a9c0dbcb2ff3a353227222c508b049eac76ba","observation_id":"4660d443-3e78-47c4-ae46-7bba309efa82","resolution":{"observed_at":"2026-05-22T10:31:25.514134Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2605.12061","last_updated":"2026-05-12T12:47:43Z","snapshot_observed_at":"2026-08-06T07:22:02.201477Z","submitted_at":"2026-05-12T12:47:43Z","title":"SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory","version":1},"reference_index":122,"source":"arxiv_source","source_observed_at":"2026-05-13T04:45:34.957298Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2605.12061"},"observation_digest":"sha256:aa282606003ee828cf12fc47873f0b27805981d40c2f6e6f34edc34e226a68c0","observation_id":"a8740c23-cacd-45e1-84f9-d438f6895079","resolution":{"observed_at":"2026-05-13T04:52:17.406931Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2605.18579","last_updated":"2026-05-20T03:15:37Z","snapshot_observed_at":"2026-08-02T23:11:13.880944Z","submitted_at":"2026-05-18T15:56:19Z","title":"S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-20T12:49:18.671971Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2605.18579"},"observation_digest":"sha256:979de45809f81d0acc53a8edb2703986b56d0da4d6758c9298438fa33292ea3c","observation_id":"6c8ec294-b20d-4b3a-8ebe-87008b0c1f03","resolution":{"observed_at":"2026-05-20T12:53:17.643242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2605.18579","last_updated":"2026-05-20T03:15:37Z","snapshot_observed_at":"2026-08-02T23:11:13.880944Z","submitted_at":"2026-05-18T15:56:19Z","title":"S2Aligner: Pair-Efficient and Transferable Pre-Training for Sparse Text-Attributed Graphs","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-21T07:55:11.088587Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2605.18579"},"observation_digest":"sha256:6ef71d3838f0e45a03f00b759db3a5af0d4c4ce09b7ad50888ed2f0025cb6b75","observation_id":"85db4da7-f0b8-45d4-982d-3b1a63b4ca9b","resolution":{"observed_at":"2026-05-21T07:59:50.870907Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.01873","last_updated":"2026-06-01T08:19:47Z","snapshot_observed_at":"2026-08-01T22:20:59.708763Z","submitted_at":"2026-06-01T08:19:47Z","title":"G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T15:25:05.566939Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.01873"},"observation_digest":"sha256:36f739cdeb77c568ec87abc9b0949e45c9b86fe191e329fe46cd8c7c8d6bb284","observation_id":"ddad0c45-faf9-4d3d-a23d-c3b25fa28f58","resolution":{"observed_at":"2026-07-01T22:26:17.559022Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.03307","last_updated":"2026-06-03T03:07:00Z","snapshot_observed_at":"2026-08-07T14:34:07.062842Z","submitted_at":"2026-06-02T08:21:57Z","title":"Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T08:25:09.062736Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.03307"},"observation_digest":"sha256:9fb3d4e413bc55a9d0a652e17fb9e96bfd02ca56fefd25ba722149b738fa56c3","observation_id":"9b5dde4d-5d4b-48e6-866e-5b31dc00aa37","resolution":{"observed_at":"2026-07-02T05:16:39.222272Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.03315","last_updated":"2026-06-02T08:27:03Z","snapshot_observed_at":"2026-08-02T01:16:29.098858Z","submitted_at":"2026-06-02T08:27:03Z","title":"A Graph Foundation Model with Spectral Parsing and Prototype-Guided Spatial Propagation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T11:34:22.107760Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.03315"},"observation_digest":"sha256:419d5a6a5a2c79bdbc1c88a077b2891fdd727ddfb01af6ff6295fb3cc8754949","observation_id":"ca61133d-b760-41e8-b392-b17d69a52acf","resolution":{"observed_at":"2026-07-02T01:46:26.718986Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.11562","last_updated":"2026-06-10T01:41:53Z","snapshot_observed_at":"2026-08-05T13:59:17.197329Z","submitted_at":"2026-06-10T01:41:53Z","title":"GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-27T10:41:37.290485Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.11562"},"observation_digest":"sha256:c844139fb02afdc14bd84daa18f1edd2593c3ed73d468de30b48a5056ad66fec","observation_id":"8fd29ed7-3384-4ece-bbff-5e7a224758a9","resolution":{"observed_at":"2026-07-03T08:57:47.494566Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.11583","last_updated":"2026-06-10T02:15:56Z","snapshot_observed_at":"2026-07-06T23:50:39.386691Z","submitted_at":"2026-06-10T02:15:56Z","title":"Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T10:33:02.954683Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.11583"},"observation_digest":"sha256:fef0516dc28c19e4954e5c2db913d35d3867a26333eb3613e3720e5ebfdc7218","observation_id":"f1a77764-3b06-49d5-b3ea-1f558c8e8039","resolution":{"observed_at":"2026-07-03T09:07:47.681980Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.17667","last_updated":"2026-06-16T08:29:34Z","snapshot_observed_at":"2026-08-03T03:23:46.290922Z","submitted_at":"2026-06-16T08:29:34Z","title":"Handling Feature Heterogeneity with Learnable Graph Patches","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-27T01:36:45.977332Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.17667"},"observation_digest":"sha256:f060b999f2292a8033620cc2928511f84e71f7457af12fcbc207584c83a5074e","observation_id":"7cee52e5-483a-46cd-ac87-d411a3d597e3","resolution":{"observed_at":"2026-07-03T20:08:56.260274Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.29773","last_updated":"2026-06-29T04:30:45Z","snapshot_observed_at":"2026-07-31T06:33:27.649714Z","submitted_at":"2026-06-29T04:30:45Z","title":"GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T07:33:27.135616Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.29773"},"observation_digest":"sha256:aff95cd1233fc241da7aded5bb7ccd0aa770c8b7bfe7bb105128bba345237f63","observation_id":"478838e7-e4ee-42a6-b82a-35f2bf87df8d","resolution":{"observed_at":"2026-06-30T07:34:21.262611Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":"2310.00149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-03T20:08:56.258128Z","title":"One for all: Towards training one graph model for all classifi- cation tasks","venue":null,"work_id":"34b1ef59-3652-420a-aa23-7b03bc35b175","year":2023},"citing_paper":{"arxiv_id":"2606.30291","last_updated":"2026-06-29T13:35:05Z","snapshot_observed_at":"2026-08-03T01:16:02.602622Z","submitted_at":"2026-06-29T13:35:05Z","title":"PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T06:06:46.108334Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2606.30291"},"observation_digest":"sha256:e94cc3cad7dd58842ab4cdc8c6c9603ce7641da4bd437ccc7d7d5492191b3b99","observation_id":"129bed47-7e79-40e5-9b46-1816ba84e903","resolution":{"observed_at":"2026-06-30T08:24:26.856671Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-07-14T13:51:14.141383Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10159","last_updated":"2026-07-11T06:47:26Z","snapshot_observed_at":"2026-08-01T19:51:36.190580Z","submitted_at":"2026-07-11T06:47:26Z","title":"UNIT: Unleash Large Language Models Potential for Graph Continual Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T13:51:14.141383Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2607.10159"},"observation_digest":"sha256:026dcd7a4037c0e6fad3e97d3c7ba0ebff31964938aa29cb945724ed8019708e","observation_id":"36a49855-3893-4454-ad04-7bd8d4f4c9a9","resolution":{"observed_at":"2026-07-14T13:51:14.141383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-01T13:29:54.619115Z","title":"International Conference on Learning Representations , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19108","last_updated":"2026-07-21T13:50:24Z","snapshot_observed_at":"2026-08-08T00:54:55.183688Z","submitted_at":"2026-07-21T13:50:24Z","title":"OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T13:29:54.619115Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2607.19108"},"observation_digest":"sha256:beffbe4d2d880bcdf9b606ec8b9bfa958b18953cc6174d9e50aa82dec90d9c4b","observation_id":"c59ff686-bab1-41c0-8da5-3539228d178c","resolution":{"observed_at":"2026-08-01T13:29:54.619115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00149","snapshot_observed_at":"2026-08-03T16:12:17.694736Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.28980","last_updated":"2026-07-31T03:12:09Z","snapshot_observed_at":"2026-08-09T03:25:54.058940Z","submitted_at":"2026-07-31T03:12:09Z","title":"Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T16:12:17.694736Z"},"links":{"cited_paper":"/paper/2310.00149","citing_paper":"/paper/2607.28980"},"observation_digest":"sha256:73450c38691a4534ce5ff0c30ad74b116de8f3bdd4da12c14cbbfc9fd6849477","observation_id":"5c741137-c652-4ae5-b428-d3227bd658da","resolution":{"observed_at":"2026-08-03T16:12:17.694736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2310.00149/citation-record","integrity":"/paper/2310.00149/integrity","json":"/paper/2310.00149/citation-record.json","paper":"/paper/2310.00149"},"outbound":[],"paper":{"arxiv_id":"2310.00149","last_updated":"2024-07-12T23:01:32Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T16:25:46.699214Z","submitted_at":"2023-09-29T21:15:26Z","title":"One for All: Towards Training One Graph Model for All Classification Tasks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2310.00149."}