{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:M7U4WTL3IATIZBP5NEYMLBXJ7C","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"33bc097cef0d276205fcec778aa849edbd2d661093065d9035a47855ea3af1f3","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2019-06-27T23:34:45Z","title_canon_sha256":"bff352a5f6b3629de5ee7c3d65e191c2e161af349f76854e0c0b76b6577af2c4"},"schema_version":"1.0","source":{"id":"1906.11994","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.11994","created_at":"2026-07-05T01:46:11Z"},{"alias_kind":"arxiv_version","alias_value":"1906.11994v3","created_at":"2026-07-05T01:46:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.11994","created_at":"2026-07-05T01:46:11Z"},{"alias_kind":"pith_short_12","alias_value":"M7U4WTL3IATI","created_at":"2026-07-05T01:46:11Z"},{"alias_kind":"pith_short_16","alias_value":"M7U4WTL3IATIZBP5","created_at":"2026-07-05T01:46:11Z"},{"alias_kind":"pith_short_8","alias_value":"M7U4WTL3","created_at":"2026-07-05T01:46:11Z"}],"graph_snapshots":[{"event_id":"sha256:93706b7ec10699800f6437f52442176c6f6127e5186e64d2a7b0c0c0fa0d5886","target":"graph","created_at":"2026-07-05T01:46:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1906.11994/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more complicated than in Euclidian space, recent studies have extensively utilized neural nets to effectively and efficiently embed a graph's nodes into a multidimensional space. However, this embedding method has not yet been applied to large-scale bipartite graphs. Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite gra","authors_text":"Chaoyang He, Cyrus Shahabi, Junzhou Huang, Tian Xie, Wenbing Huang, Xiang Ren, Yu Rong","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2019-06-27T23:34:45Z","title":"Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.11994","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:48aa8a6bd0e157d9d02bbdf2e7138f10c9139693cd5455593111542272de95a7","target":"record","created_at":"2026-07-05T01:46:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"33bc097cef0d276205fcec778aa849edbd2d661093065d9035a47855ea3af1f3","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2019-06-27T23:34:45Z","title_canon_sha256":"bff352a5f6b3629de5ee7c3d65e191c2e161af349f76854e0c0b76b6577af2c4"},"schema_version":"1.0","source":{"id":"1906.11994","kind":"arxiv","version":3}},"canonical_sha256":"67e9cb4d7b40268c85fd6930c586e9f8ad526b984e17b23b314eef6abaf09a83","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67e9cb4d7b40268c85fd6930c586e9f8ad526b984e17b23b314eef6abaf09a83","first_computed_at":"2026-07-05T01:46:11.582402Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:46:11.582402Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LVTv7qy3bo1h4j1Umj2pRXq/7Si6PS8vh+mA5WTv6mSF3fcotZ3Tygh0v/LQZEGTkuP0ROzyhTdVl/nU0C63AA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:46:11.583036Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.11994","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:48aa8a6bd0e157d9d02bbdf2e7138f10c9139693cd5455593111542272de95a7","sha256:93706b7ec10699800f6437f52442176c6f6127e5186e64d2a7b0c0c0fa0d5886"],"state_sha256":"6b7684df79fca87ca4b0eb5d7dd83dc6d5b858cadec01c2ae63ad04ef90fbb61"}