{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:UW4V4LBWTK4QYYZQTMOUSPZ23A","short_pith_number":"pith:UW4V4LBW","schema_version":"1.0","canonical_sha256":"a5b95e2c369ab90c63309b1d493f3ad83974d2982d747b73324a4428e6f4476d","source":{"kind":"arxiv","id":"2004.00216","version":3},"attestation_state":"computed","paper":{"title":"Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Carl Yang, Jiawei Han, Yizhou Sun, Yuxin Xiao, Yu Zhang","submitted_at":"2020-04-01T03:42:11Z","abstract_excerpt":"Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (\\aka~embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has "},"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":"2004.00216","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T03:42:11Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b924550c44f26019e173eef0cc7363b2a7e127f0dbfdd33bc64376ebad16eeaa","abstract_canon_sha256":"a6b5f81843f52b176ddf492b08e97c4dadf8aa0f2c0cf0438fbecfd03d547b90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:00:10.281565Z","signature_b64":"aDxWZuPXWvyhKTV6LN21k7aVhMDgYmNYEO4vD+SM7xKsFd3Uceff3u8zxtJ658a7IEo7tlVWDgxADw+LQEheCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5b95e2c369ab90c63309b1d493f3ad83974d2982d747b73324a4428e6f4476d","last_reissued_at":"2026-07-05T02:00:10.281155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:00:10.281155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Carl Yang, Jiawei Han, Yizhou Sun, Yuxin Xiao, Yu Zhang","submitted_at":"2020-04-01T03:42:11Z","abstract_excerpt":"Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (\\aka~embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.00216","kind":"arxiv","version":3},"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/2004.00216/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":"2004.00216","created_at":"2026-07-05T02:00:10.281213+00:00"},{"alias_kind":"arxiv_version","alias_value":"2004.00216v3","created_at":"2026-07-05T02:00:10.281213+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.00216","created_at":"2026-07-05T02:00:10.281213+00:00"},{"alias_kind":"pith_short_12","alias_value":"UW4V4LBWTK4Q","created_at":"2026-07-05T02:00:10.281213+00:00"},{"alias_kind":"pith_short_16","alias_value":"UW4V4LBWTK4QYYZQ","created_at":"2026-07-05T02:00:10.281213+00:00"},{"alias_kind":"pith_short_8","alias_value":"UW4V4LBW","created_at":"2026-07-05T02:00:10.281213+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/UW4V4LBWTK4QYYZQTMOUSPZ23A","json":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A.json","graph_json":"https://pith.science/api/pith-number/UW4V4LBWTK4QYYZQTMOUSPZ23A/graph.json","events_json":"https://pith.science/api/pith-number/UW4V4LBWTK4QYYZQTMOUSPZ23A/events.json","paper":"https://pith.science/paper/UW4V4LBW"},"agent_actions":{"view_html":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A","download_json":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A.json","view_paper":"https://pith.science/paper/UW4V4LBW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2004.00216&json=true","fetch_graph":"https://pith.science/api/pith-number/UW4V4LBWTK4QYYZQTMOUSPZ23A/graph.json","fetch_events":"https://pith.science/api/pith-number/UW4V4LBWTK4QYYZQTMOUSPZ23A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A/action/storage_attestation","attest_author":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A/action/author_attestation","sign_citation":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A/action/citation_signature","submit_replication":"https://pith.science/pith/UW4V4LBWTK4QYYZQTMOUSPZ23A/action/replication_record"}},"created_at":"2026-07-05T02:00:10.281213+00:00","updated_at":"2026-07-05T02:00:10.281213+00:00"}