{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AG2JAH3OFKGC6EDWYPWPC4HGEJ","short_pith_number":"pith:AG2JAH3O","schema_version":"1.0","canonical_sha256":"01b4901f6e2a8c2f1076c3ecf170e6226555c3fbcbb8441781ad3d05b0535283","source":{"kind":"arxiv","id":"2505.10040","version":1},"attestation_state":"computed","paper":{"title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaxing Li, Lei Song, Shihan Guan, Youyong Kong","submitted_at":"2025-05-15T07:35:27Z","abstract_excerpt":"Graph Neural Networks (GNN) endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge amid the assimilation of novel information. Rehearsal-based techniques revisit historical examples, adopted as a principal strategy to alleviate this phenomenon. However, memory explosion and privacy infringements impose significant constraints on their utility. Non-Exemplar methods circumvent the prior issues through Prototype Replay (PR), yet feature drift presents new challenges. In this paper, our empirical findings reveal that Prototype Contrastive Learning (PCL"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.10040","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-15T07:35:27Z","cross_cats_sorted":[],"title_canon_sha256":"0253682da75ce19d6723c82938fd7ad3b70608d00ef25668bbff0ff90ef1a2a4","abstract_canon_sha256":"83c8d27b9b5cd4a0b771d250c052c334b863efa1cbd48cc6835ae41b5b4fe942"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:03:32.264232Z","signature_b64":"Bj6aMOc18KyGoUfsXcfCFlDM9TKWj1NVr+vNbH6b0pcgewJ0vH22m0hggeqXqvIjordJuxXVAgsWQED6nEt6Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01b4901f6e2a8c2f1076c3ecf170e6226555c3fbcbb8441781ad3d05b0535283","last_reissued_at":"2026-07-05T11:03:32.263789Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:03:32.263789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaxing Li, Lei Song, Shihan Guan, Youyong Kong","submitted_at":"2025-05-15T07:35:27Z","abstract_excerpt":"Graph Neural Networks (GNN) endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge amid the assimilation of novel information. Rehearsal-based techniques revisit historical examples, adopted as a principal strategy to alleviate this phenomenon. However, memory explosion and privacy infringements impose significant constraints on their utility. Non-Exemplar methods circumvent the prior issues through Prototype Replay (PR), yet feature drift presents new challenges. In this paper, our empirical findings reveal that Prototype Contrastive Learning (PCL"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.10040","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.10040/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.10040","created_at":"2026-07-05T11:03:32.263847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.10040v1","created_at":"2026-07-05T11:03:32.263847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.10040","created_at":"2026-07-05T11:03:32.263847+00:00"},{"alias_kind":"pith_short_12","alias_value":"AG2JAH3OFKGC","created_at":"2026-07-05T11:03:32.263847+00:00"},{"alias_kind":"pith_short_16","alias_value":"AG2JAH3OFKGC6EDW","created_at":"2026-07-05T11:03:32.263847+00:00"},{"alias_kind":"pith_short_8","alias_value":"AG2JAH3O","created_at":"2026-07-05T11:03:32.263847+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ","json":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ.json","graph_json":"https://pith.science/api/pith-number/AG2JAH3OFKGC6EDWYPWPC4HGEJ/graph.json","events_json":"https://pith.science/api/pith-number/AG2JAH3OFKGC6EDWYPWPC4HGEJ/events.json","paper":"https://pith.science/paper/AG2JAH3O"},"agent_actions":{"view_html":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ","download_json":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ.json","view_paper":"https://pith.science/paper/AG2JAH3O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.10040&json=true","fetch_graph":"https://pith.science/api/pith-number/AG2JAH3OFKGC6EDWYPWPC4HGEJ/graph.json","fetch_events":"https://pith.science/api/pith-number/AG2JAH3OFKGC6EDWYPWPC4HGEJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ/action/storage_attestation","attest_author":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ/action/author_attestation","sign_citation":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ/action/citation_signature","submit_replication":"https://pith.science/pith/AG2JAH3OFKGC6EDWYPWPC4HGEJ/action/replication_record"}},"created_at":"2026-07-05T11:03:32.263847+00:00","updated_at":"2026-07-05T11:03:32.263847+00:00"}