{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VITAFAMDKER2SKRJ6I7HRPPKHF","short_pith_number":"pith:VITAFAMD","schema_version":"1.0","canonical_sha256":"aa260281835123a92a29f23e78bdea396f3d3050f4d7c1a16c9ecadd30f532e1","source":{"kind":"arxiv","id":"2508.14859","version":1},"attestation_state":"computed","paper":{"title":"Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiafeng Xiong, Rizos Sakellariou","submitted_at":"2025-08-20T17:13:19Z","abstract_excerpt":"Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph information. We propose GTGIB, a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB). We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods and demonstrate its effectiveness and efficiency throu"},"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":"2508.14859","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-20T17:13:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"083e5d3cc55e3883a4c1d015f76526552502af0c762071f664e1af1252aceaa3","abstract_canon_sha256":"a90c72b96f7d590d45f189a62133d10824c50791c4b3a067ed90656d9b418663"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:45.589655Z","signature_b64":"4LCDxbxOCczvYDJu8vk0CROb0/YzabkB+z52qbtVPwfuAwIbgKM8Jlnyg/SgsJwF/tn5D20yUFHDEOIQBxKqDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa260281835123a92a29f23e78bdea396f3d3050f4d7c1a16c9ecadd30f532e1","last_reissued_at":"2026-07-05T11:56:45.589170Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:45.589170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jiafeng Xiong, Rizos Sakellariou","submitted_at":"2025-08-20T17:13:19Z","abstract_excerpt":"Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive representation learning in such settings faces two major challenges: effectively representing unseen nodes and mitigating noisy or redundant graph information. We propose GTGIB, a versatile framework that integrates Graph Structure Learning (GSL) with Temporal Graph Information Bottleneck (TGIB). We design a novel two-step GSL-based structural enhancer to enrich and optimize node neighborhoods and demonstrate its effectiveness and efficiency throu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.14859","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/2508.14859/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":"2508.14859","created_at":"2026-07-05T11:56:45.589228+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.14859v1","created_at":"2026-07-05T11:56:45.589228+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.14859","created_at":"2026-07-05T11:56:45.589228+00:00"},{"alias_kind":"pith_short_12","alias_value":"VITAFAMDKER2","created_at":"2026-07-05T11:56:45.589228+00:00"},{"alias_kind":"pith_short_16","alias_value":"VITAFAMDKER2SKRJ","created_at":"2026-07-05T11:56:45.589228+00:00"},{"alias_kind":"pith_short_8","alias_value":"VITAFAMD","created_at":"2026-07-05T11:56:45.589228+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/VITAFAMDKER2SKRJ6I7HRPPKHF","json":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF.json","graph_json":"https://pith.science/api/pith-number/VITAFAMDKER2SKRJ6I7HRPPKHF/graph.json","events_json":"https://pith.science/api/pith-number/VITAFAMDKER2SKRJ6I7HRPPKHF/events.json","paper":"https://pith.science/paper/VITAFAMD"},"agent_actions":{"view_html":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF","download_json":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF.json","view_paper":"https://pith.science/paper/VITAFAMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.14859&json=true","fetch_graph":"https://pith.science/api/pith-number/VITAFAMDKER2SKRJ6I7HRPPKHF/graph.json","fetch_events":"https://pith.science/api/pith-number/VITAFAMDKER2SKRJ6I7HRPPKHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF/action/storage_attestation","attest_author":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF/action/author_attestation","sign_citation":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF/action/citation_signature","submit_replication":"https://pith.science/pith/VITAFAMDKER2SKRJ6I7HRPPKHF/action/replication_record"}},"created_at":"2026-07-05T11:56:45.589228+00:00","updated_at":"2026-07-05T11:56:45.589228+00:00"}