{"as_of":"2026-08-17T22:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53a4217e14f66624306d39ccc18626586bb10df5d4e465dfde8fd015a6fb396e","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:21:12.762470Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.10040/citation-record","integrity":"/paper/2505.10040/integrity","json":"/paper/2505.10040/citation-record.json","paper":"/paper/2505.10040"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.594244Z","title":"Memory aware synapses: Learning what (not) to forget","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.594244Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:b9750f7a3201473b57acaa884a3c876eaedaa1013ced8fb198b66378f1e37c1b","observation_id":"3d5aa948-30d2-4d33-aec6-246e1da1c0c4","resolution":{"observed_at":"2026-08-15T21:21:12.594244Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12604","last_updated":"2022-05-10T09:32:26Z","snapshot_observed_at":"2026-08-17T02:47:00.352374Z","submitted_at":"2022-01-29T15:15:23Z","title":"Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12604","snapshot_observed_at":"2026-08-15T21:21:12.598080Z","title":"Learning fast, learning slow: A general contin- ual learning method based on complementary learning system.arXiv preprint arXiv:2201.12604, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.598080Z"},"links":{"cited_paper":"/paper/2201.12604","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:274e98448961bb289379793929ace4ddc7be81e50db0d364f7d9bd55a77f58f8","observation_id":"719322c6-8859-4df9-a68d-383d85cbee0a","resolution":{"observed_at":"2026-08-15T21:21:12.598080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.284478Z","title":"Mm-gnn: Mix-moment graph neural network towards modeling neighborhood feature distribution","venue":null,"work_id":"437285cb-2d83-49fd-838d-2af5c1d0ddd2","year":2023},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.602549Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:0370d1db805e5d9217240849efa8075b403c1966f0377ba2829238b464c34fac","observation_id":"7222c57c-1e03-4344-a166-9a4b76e4ed4f","resolution":{"observed_at":"2026-08-15T21:21:13.288207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.272836Z","title":"Efficient statistical sampling adaptation for exemplar-free class incremental learning.IEEE Transactions on Circuits and Systems for Video Technology, 2024","venue":null,"work_id":"6756c8f6-efa9-4539-8352-112ba2229c2b","year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.606796Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:6e589fee802e6d6394e29592ebcad8615ecf7e17f23c222c24f7c0b26b166b87","observation_id":"5285d065-3029-4e3f-9785-6229a1ea6cf3","resolution":{"observed_at":"2026-08-15T21:21:13.276509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.259173Z","title":"Protognn: Prototype-assisted message passing framework for non-homophilous graphs","venue":null,"work_id":"2e6f920b-413f-4ea3-ab09-cdf62de910a0","year":2022},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.610667Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:cd229f66959f0d37d05645b428b8b546fa2204e01556f49b217d618e494f2b5a","observation_id":"42e1f8e7-462f-4e79-838a-ecf6de3fe5a1","resolution":{"observed_at":"2026-08-15T21:21:13.264268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.245874Z","title":"Exemplar-free continual representation learning via learnable drift compensation","venue":null,"work_id":"968a4743-41a3-4a9f-a982-b536f7c7ccc8","year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.614786Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:c6bfe30f21579e325dc3ae2b33439f0307d7c40c89883e4fcddee3bbac6443a4","observation_id":"f10cadb2-7dd6-4c14-8e62-88fae506102e","resolution":{"observed_at":"2026-08-15T21:21:13.250253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.619300Z","title":"Inductive representation learning on large graphs.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.619300Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:1141186c261c0e7fda6fc3c8eac3b859a56e2ff06a1481299d76d68162b25008","observation_id":"a1c8012f-25fb-4477-bdc2-910ae88735b2","resolution":{"observed_at":"2026-08-15T21:21:12.619300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.218999Z","title":"Contrastive multi-view representation learning on graphs","venue":null,"work_id":"08a11afd-d341-47c3-adfd-ab688b9126cc","year":2020},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.623198Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:afddca48e461ac674d4e419967a2b2e762cbce1497317fa1eabaf045cf0384b6","observation_id":"9f62bb15-545b-4479-a163-4defe8c2f606","resolution":{"observed_at":"2026-08-15T21:21:13.223134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-08-16T18:00:58.008096Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-15T21:21:12.626862Z","title":"Distilling the knowledge in a neural network","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.626862Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:77ecc0dcb1b44ab157e69a25af5898b19d573fe0590f0041d999f3cfb963dd11","observation_id":"04f6eb88-0ee3-43f0-94d5-c80b861a97ef","resolution":{"observed_at":"2026-08-15T21:21:12.626862Z","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":"2026-08-15T21:21:12.630787Z","title":"Open graph benchmark: Datasets for machine learning on graphs","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.630787Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:941b7769851edaf9dc6a811c66c72517f59060ee65d8e0b1bd46e2ee920932f9","observation_id":"8b184e42-d8eb-4423-b7a9-ef6cd4b397ec","resolution":{"observed_at":"2026-08-15T21:21:12.630787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-15T21:21:12.634571Z","title":"Semi-supervised classification with graph convolutional networks.arXiv preprint arXiv:1609.02907, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.634571Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:a602a80dd83bee4037475b0836822d2266c8429bce4c05caa5ff832dcd32ca16","observation_id":"0bf674e8-101a-4a15-abf1-8e3a64d035f6","resolution":{"observed_at":"2026-08-15T21:21:12.634571Z","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":"2026-08-15T21:21:12.638914Z","title":"Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.638914Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:3ce3bad438b033aa452b382e25953370e89b0fdd8c9fa1b99e7d86d000a7152d","observation_id":"7c4d9aa2-88cd-4845-83d3-c33e8c952fda","resolution":{"observed_at":"2026-08-15T21:21:12.638914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.190963Z","title":"Fcs: Feature calibration and separation for non- exemplar class incremental learning","venue":null,"work_id":"44030449-1c09-45d7-91d0-9a6bfbfb3c30","year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.642760Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:9bf21f94df10a81ba5bc1cf481331ccdc9e5acb765412a7ab6d2ee22448e598d","observation_id":"a2766259-17fd-45cd-8e96-d3c62a54b9f0","resolution":{"observed_at":"2026-08-15T21:21:13.194649Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.177982Z","title":"Inductive graph few-shot class incremental learning","venue":null,"work_id":"d9417bc3-fdbc-4b71-9b97-579cfba5f14c","year":2025},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.646519Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:fe56f1cf1c468460cf5b229a6e11fd6e6e5c83449675306a785572dae5eb55c7","observation_id":"bbd76f73-5a2c-4d06-a776-657b05646ae4","resolution":{"observed_at":"2026-08-15T21:21:13.182373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.650414Z","title":"Learning without forgetting.IEEE transactions on pattern analysis and machine intelligence, 40(12):2935–2947, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.650414Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:f4c6baeb7b2d1b7bb97cb683c146a5f5731f13a219751532f05deeae40aaa53b","observation_id":"abc9678c-49f1-4349-9e10-84b60346768c","resolution":{"observed_at":"2026-08-15T21:21:12.650414Z","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":"2026-08-15T21:21:12.654143Z","title":"Overcoming catastrophic forgetting in graph neural networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.654143Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:7b2660d7f7e7af171e2a70a455102184741a47ef879c1fe390f5db8566872319","observation_id":"8ae59778-93d6-4540-a431-a4886888208f","resolution":{"observed_at":"2026-08-15T21:21:12.654143Z","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":"2026-08-15T21:21:12.657878Z","title":"Cat: Balanced continual graph learning with graph condensation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.657878Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:e350b239730935978b9861ea8970f38f2d602308ad45a0698b89b23730f9d495","observation_id":"caee6416-bef6-469f-a877-2feec7578d7e","resolution":{"observed_at":"2026-08-15T21:21:12.657878Z","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":"2026-08-15T21:21:12.661797Z","title":"Gradient episodic memory for continual learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.661797Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:dad8022f63303a1ad22a0168b6f5485850753445385c9eaaa973fc619d55825a","observation_id":"a1f78f84-5327-43c4-9177-0c845c8788bc","resolution":{"observed_at":"2026-08-15T21:21:12.661797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03917","last_updated":"2024-05-30T09:15:06Z","snapshot_observed_at":"2026-08-16T14:21:03.055373Z","submitted_at":"2024-02-06T11:35:02Z","title":"Elastic Feature Consolidation for Cold Start Exemplar-Free Incremental Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03917","snapshot_observed_at":"2026-08-15T21:21:12.665498Z","title":"Elastic feature consolidation for cold start exemplar-free incremental learning.arXiv preprint arXiv:2402.03917, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.665498Z"},"links":{"cited_paper":"/paper/2402.03917","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:63c86874158b70aa569ac1943bac21838edb8394b9265fceca422d4f6497be5d","observation_id":"429fca47-54b5-44a2-93c4-03fee5d9901c","resolution":{"observed_at":"2026-08-15T21:21:12.665498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.133136Z","title":"Automating the construction of internet portals with machine learning.Information Retrieval, 3:127–163, 2000","venue":null,"work_id":"7539571f-cef3-4790-9309-6c53b30915b0","year":2000},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.669833Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:a85178ff99ad5163b655e313410963aee176e9127b4379725178b0a9a0fc3666","observation_id":"2f1ee6b4-1d54-4b6c-ae13-1dbe917af221","resolution":{"observed_at":"2026-08-15T21:21:13.137480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.673650Z","title":"Distributed repre- sentations of words and phrases and their compositionality.Advances in neural information processing systems, 26, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.673650Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:215f26dc51655d3c686015dbbf4a9e35cce188a81cdfdd27207798cfd6920bcc","observation_id":"7684b477-9ebe-4e18-a845-d8d8034cf6de","resolution":{"observed_at":"2026-08-15T21:21:12.673650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10341","last_updated":"2024-10-28T00:01:22Z","snapshot_observed_at":"2026-08-16T13:09:41.431407Z","submitted_at":"2024-10-14T09:54:20Z","title":"Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10341","snapshot_observed_at":"2026-08-15T21:21:12.677418Z","title":"Replay-and-forget-free graph class-incremental learning: A task profiling and prompting approach.arXiv preprint arXiv:2410.10341, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.677418Z"},"links":{"cited_paper":"/paper/2410.10341","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:05bff1728213a6b7847257e6730f6960012b42072c0b9a09f79e801be306bd02","observation_id":"fba58cba-b8d2-4aad-bd97-cd7951a694f3","resolution":{"observed_at":"2026-08-15T21:21:12.677418Z","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":"2026-08-15T21:21:12.681568Z","title":"The pagerank citation ranking: Bringing order to the web","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.681568Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:efaa2361c9ba58864f1c15f804f12830473aaacbb1d46b5ea87bfd8636b7d213","observation_id":"864f9992-9856-4cbc-87df-0c5914686b24","resolution":{"observed_at":"2026-08-15T21:21:12.681568Z","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":"2026-08-15T21:21:12.685214Z","title":"Fetril: Feature translation for exemplar-free class-incremental learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.685214Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:c40dd298397ee935ba15581f473e9a804a8d8d0a7cd0cc7b8173110e0071ebef","observation_id":"53668f2d-2ccd-459c-a264-0e7733c98df6","resolution":{"observed_at":"2026-08-15T21:21:12.685214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.096670Z","title":"Incremental graph classification by class prototype construction and augmentation","venue":null,"work_id":"b36d731d-1e4c-4108-8516-1e0dc189593f","year":2023},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.688957Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:ec973068f9e7e158d587953ffd3e0a0a9c7f70882b263fbd4ae4b1e24aafa085","observation_id":"3e9a3309-cec5-4bea-9f9f-7ce81a18a619","resolution":{"observed_at":"2026-08-15T21:21:13.100978Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.083819Z","title":"Graph neural networks: Architectures, stability, and transferability.Proceedings of the IEEE, 109(5):660–682, 2021","venue":null,"work_id":"13026580-42b1-4dfd-ba2a-a540678ded3a","year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.692506Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:4e07b67085da71c8a7ad0eedbecc1c64d7aec880ff2e369249ad995aedb91e11","observation_id":"ca6df8e8-ed08-487f-8001-ba5646ccb13d","resolution":{"observed_at":"2026-08-15T21:21:13.088103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.071357Z","title":"The lattice theory of information.Transactions of the IRE professional Group on Information Theory, 1(1):105–107, 1953","venue":null,"work_id":"6e4b9ffd-917e-491c-9e59-72316ea2a9b1","year":1953},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.696003Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:57618b9e8a17fb0d91be7339729f4bde102b7d4daa6f892650afb70c617292cf","observation_id":"b687d5f4-9d4b-4e1c-bdf0-7b95889487c7","resolution":{"observed_at":"2026-08-15T21:21:13.075406Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.700060Z","title":"A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948","venue":null,"work_id":null,"year":1948},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.700060Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:11e93df9c39cf932c1f6e975a24675c89c2be045780f2d8bd33ded6d0b3ac556","observation_id":"d692aa43-5c14-4d44-b093-7285c0e33c0d","resolution":{"observed_at":"2026-08-15T21:21:12.700060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.05868","last_updated":"2019-06-18T13:15:39Z","snapshot_observed_at":"2026-08-14T17:59:20.424546Z","submitted_at":"2018-11-14T15:53:19Z","title":"Pitfalls of Graph Neural Network Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.05868","snapshot_observed_at":"2026-08-15T21:21:12.704066Z","title":"Pitfalls of graph neural network evaluation.arXiv preprint arXiv:1811.05868, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.704066Z"},"links":{"cited_paper":"/paper/1811.05868","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:a168531f141999c81efac27b818bd566a5c7a12857060ba7f93e8caa08d01adf","observation_id":"5fa1a21b-f643-4f9b-8575-fa249845bab7","resolution":{"observed_at":"2026-08-15T21:21:12.704066Z","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":"2026-08-15T21:21:12.707960Z","title":"Prototypical networks for few-shot learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.707960Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:b9e284b4db96aba2f01f12e61ac1d565fa9a660e53089575df087255b0bdf212","observation_id":"d03909fa-cdc1-4b87-a320-a85f471f47c3","resolution":{"observed_at":"2026-08-15T21:21:12.707960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-15T21:21:12.711725Z","title":"Graph attention networks.arXiv preprint arXiv:1710.10903, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.711725Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:63e5c8467441f8ab88282a63f20f6dedc0a322c1ab6acab98d3576f5824cc300","observation_id":"0aa6a432-df9a-44a9-8ab2-1e3f71073065","resolution":{"observed_at":"2026-08-15T21:21:12.711725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.041133Z","title":"Non-exemplar class- incremental learning via adaptive old class reconstruction","venue":null,"work_id":"07fb9a77-9525-48d5-8e45-f7c5d499ddfe","year":2023},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.714922Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:1c16ba20df401702ef87d36bce96152ba7e0e2e8f45def79e3a99fc6808f83bb","observation_id":"4691901a-70f3-424d-b32b-36fe568be063","resolution":{"observed_at":"2026-08-15T21:21:13.046201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.029349Z","title":"Mixup for node and graph classification","venue":null,"work_id":"ba35bc1a-bda9-482b-968d-a792dd85cd6d","year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.718194Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:fe75e81e306ba4aedabe60005ab485a61f4b91a69cc8692ec7db5d0ed95fbdc7","observation_id":"c82ac3e0-bff7-4f00-a02c-e6921b9b6d86","resolution":{"observed_at":"2026-08-15T21:21:13.032981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.017943Z","title":"Simplifying graph convolutional networks","venue":null,"work_id":"10ce9163-832f-4b44-baac-4faa418ef94c","year":2019},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.721529Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:03a9e6659897f6563b8a8afa4218d8fd473ec149146ac32f4adef76c906f2898","observation_id":"cecb23b6-9e7c-4a2b-a546-7cb96c8a750e","resolution":{"observed_at":"2026-08-15T21:21:13.021579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:13.005118Z","title":"Infogcl: Information-aware graph contrastive learning.Advances in Neural Information Processing Systems, 34:30414–30425, 2021","venue":null,"work_id":"d435adff-8569-47be-8374-2d4999626b87","year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.724983Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:95a37616d9e5c9af74ddc7e71d960fa5ba5fd26942d71f48d5e069bd247a1bd4","observation_id":"1bdafa59-fdf7-40a8-bcfb-acf25b03d9cc","resolution":{"observed_at":"2026-08-15T21:21:13.009301Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00826","last_updated":"2019-02-22T19:15:54Z","snapshot_observed_at":"2026-08-13T05:06:48.606308Z","submitted_at":"2018-10-01T17:11:31Z","title":"How Powerful are Graph Neural Networks?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00826","snapshot_observed_at":"2026-08-15T21:21:12.728502Z","title":"How powerful are graph neural networks?arXiv preprint arXiv:1810.00826, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.728502Z"},"links":{"cited_paper":"/paper/1810.00826","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:473a0da712d29cd65e305e8377d69c5599d64235ddf7c71147ff4d7989811f14","observation_id":"3ddc2729-1f84-42e9-b3b3-89ea433a8554","resolution":{"observed_at":"2026-08-15T21:21:12.728502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.992506Z","title":"Semantic drift compensation for class-incremental learning","venue":null,"work_id":"8ab76cdf-920d-467b-bf3c-d1bc754f5e64","year":2020},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.732020Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:202b37884e016470df42b2cb4db0cd68fb1c99701b9a42e8d8fccccb03176756","observation_id":"bf8c4c3a-1e52-49d8-a852-0a4d292112de","resolution":{"observed_at":"2026-08-15T21:21:12.996479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.735455Z","title":"Continual learning through synaptic intelligence","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.735455Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:c6cd83c16ca06f4d978193f30256ef3df3d321d9c6c047a2e153917350d2074c","observation_id":"9489b169-008f-4833-bbde-39f3693a4e0c","resolution":{"observed_at":"2026-08-15T21:21:12.735455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.971887Z","title":"Fine-grained knowledge selection and restoration for non-exemplar class incremental learning","venue":null,"work_id":"c9842013-ea5d-4635-b0e2-502c5be8b3a1","year":2024},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.738795Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:c64293a302331c14e48d4174826e706cbbdaae63ad6844ae348a9e24af42a776","observation_id":"34a38916-818b-4be3-aa95-29c2ca906e05","resolution":{"observed_at":"2026-08-15T21:21:12.975994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-15T21:21:12.742146Z","title":"mixup: Beyond empirical risk minimization.arXiv preprint arXiv:1710.09412, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.742146Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:976a4dbdc4700de14874d9b280e7dfc08470a08b99e443c5ac8fbb2d93da1351","observation_id":"b57674b5-a112-4b06-a974-688ba7d013f4","resolution":{"observed_at":"2026-08-15T21:21:12.742146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.959296Z","title":"Continual learning on dynamic graphs via parameter isolation","venue":null,"work_id":"55d27ba0-fe24-49a0-9e44-cdf7b35feef3","year":2023},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.746179Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:93aa87f8e9ff0edb24c9dfdb5abf960b86a3fca8bdb5d538572c321301f2935f","observation_id":"5f81f228-0270-49d6-bd6b-f34f4f53160f","resolution":{"observed_at":"2026-08-15T21:21:12.963322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.749974Z","title":"Cglb: Benchmark tasks for continual graph learning.Advances in Neural Information Processing Systems, 35:13006–13021, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.749974Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:620ebd431ad68344c5ecd40ca02656f5e8b54e7021994c241de2b0a89ad73804","observation_id":"31f1dd9c-cb59-4a23-9a5b-012419978d4d","resolution":{"observed_at":"2026-08-15T21:21:12.749974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.938214Z","title":"Sparsified subgraph memory for continual graph representation learning","venue":null,"work_id":"3742e357-a837-44fc-87fc-e1283042742c","year":2022},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.753895Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:8699e78b55963429df33817a54bde53d76039d9c2433d6cfccc79b3dc07a087b","observation_id":"299d6ac5-b1c9-46cb-8a61-7ce07410612a","resolution":{"observed_at":"2026-08-15T21:21:12.942616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T21:21:12.758402Z","title":"Overcoming catastrophic forgetting in graph neural networks with experience replay","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.758402Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:a74d0e75298c95708389b087acade6a6ce6ea84b573ddff03bcf3edaa851a9c2","observation_id":"18b09835-d7e9-4d05-8cac-2cb2c7d27729","resolution":{"observed_at":"2026-08-15T21:21:12.758402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:21:12.914439Z","title":"Prototype augmentation and self-supervision for incremental learning","venue":null,"work_id":"a5c97992-76fe-4d45-834d-751160fda90a","year":2021},"citing_paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T21:21:12.762470Z"},"links":{"citing_paper":"/paper/2505.10040"},"observation_digest":"sha256:373e42848b950d23fca0f04662ff4d7054a1aa4df777ddb25eb1ecc44e2f45eb","observation_id":"139ad472-8de0-4e22-b87b-2e4df0320b70","resolution":{"observed_at":"2026-08-15T21:21:12.920315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.10040","last_updated":"2025-05-15T07:35:27Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T21:15:34.924875Z","submitted_at":"2025-05-15T07:35:27Z","title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":45},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.10040."}