{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:B5TVNCRD7WEIVJ45GQW73ROFCK","short_pith_number":"pith:B5TVNCRD","schema_version":"1.0","canonical_sha256":"0f67568a23fd888aa79d342dfdc5c512b0c9ca0266974b141d0cd6584f92c08a","source":{"kind":"arxiv","id":"2311.14333","version":2},"attestation_state":"computed","paper":{"title":"Cycle Invariant Positional Encoding for Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chao Chen, Liangcai Gao, Tengfei Ma, Yusu Wang, Zhi Tang, Zuoyu Yan","submitted_at":"2023-11-24T08:15:54Z","abstract_excerpt":"Cycles are fundamental elements in graph-structured data and have demonstrated their effectiveness in enhancing graph learning models. To encode such information into a graph learning framework, prior works often extract a summary quantity, ranging from the number of cycles to the more sophisticated persistence diagram summaries. However, more detailed information, such as which edges are encoded in a cycle, has not yet been used in graph neural networks. In this paper, we make one step towards addressing this gap, and propose a structure encoding module, called CycleNet, that encodes cycle in"},"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":"2311.14333","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-24T08:15:54Z","cross_cats_sorted":[],"title_canon_sha256":"0f621a47e0f2c0fb3011274a866d698734959f3d96577dcd902e43c5712b12c4","abstract_canon_sha256":"01aad03735c1ca3ba11d35a28e9a90f8d07d485e7fcdc46a30a02d82b17d3728"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:18:32.445626Z","signature_b64":"egKQ2XxaGJ37zRHB2c103Wyme4FFNh7m5Q/RQ8yymB63TSxTHavl2OgWcUTWQB3yIgImrW4x0cIxM2cOj/PUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f67568a23fd888aa79d342dfdc5c512b0c9ca0266974b141d0cd6584f92c08a","last_reissued_at":"2026-07-05T07:18:32.445172Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:18:32.445172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cycle Invariant Positional Encoding for Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chao Chen, Liangcai Gao, Tengfei Ma, Yusu Wang, Zhi Tang, Zuoyu Yan","submitted_at":"2023-11-24T08:15:54Z","abstract_excerpt":"Cycles are fundamental elements in graph-structured data and have demonstrated their effectiveness in enhancing graph learning models. To encode such information into a graph learning framework, prior works often extract a summary quantity, ranging from the number of cycles to the more sophisticated persistence diagram summaries. However, more detailed information, such as which edges are encoded in a cycle, has not yet been used in graph neural networks. In this paper, we make one step towards addressing this gap, and propose a structure encoding module, called CycleNet, that encodes cycle in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.14333","kind":"arxiv","version":2},"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/2311.14333/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":"2311.14333","created_at":"2026-07-05T07:18:32.445228+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.14333v2","created_at":"2026-07-05T07:18:32.445228+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.14333","created_at":"2026-07-05T07:18:32.445228+00:00"},{"alias_kind":"pith_short_12","alias_value":"B5TVNCRD7WEI","created_at":"2026-07-05T07:18:32.445228+00:00"},{"alias_kind":"pith_short_16","alias_value":"B5TVNCRD7WEIVJ45","created_at":"2026-07-05T07:18:32.445228+00:00"},{"alias_kind":"pith_short_8","alias_value":"B5TVNCRD","created_at":"2026-07-05T07:18:32.445228+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/B5TVNCRD7WEIVJ45GQW73ROFCK","json":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK.json","graph_json":"https://pith.science/api/pith-number/B5TVNCRD7WEIVJ45GQW73ROFCK/graph.json","events_json":"https://pith.science/api/pith-number/B5TVNCRD7WEIVJ45GQW73ROFCK/events.json","paper":"https://pith.science/paper/B5TVNCRD"},"agent_actions":{"view_html":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK","download_json":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK.json","view_paper":"https://pith.science/paper/B5TVNCRD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.14333&json=true","fetch_graph":"https://pith.science/api/pith-number/B5TVNCRD7WEIVJ45GQW73ROFCK/graph.json","fetch_events":"https://pith.science/api/pith-number/B5TVNCRD7WEIVJ45GQW73ROFCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK/action/storage_attestation","attest_author":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK/action/author_attestation","sign_citation":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK/action/citation_signature","submit_replication":"https://pith.science/pith/B5TVNCRD7WEIVJ45GQW73ROFCK/action/replication_record"}},"created_at":"2026-07-05T07:18:32.445228+00:00","updated_at":"2026-07-05T07:18:32.445228+00:00"}