{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:YOZ3RY6FYGKJHERUP7ENZMIT2O","short_pith_number":"pith:YOZ3RY6F","canonical_record":{"source":{"id":"2004.01024","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T17:16:47Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ad45f373821ca22ed7d238775469826a1958fbf02e9f28649c9f441613b33bc4","abstract_canon_sha256":"ceb47847a865cf9a808f7ae00bbf2e99704588a591545942f2dc2d89c8ec3043"},"schema_version":"1.0"},"canonical_sha256":"c3b3b8e3c5c1949392347fc8dcb113d3bfd4e8f497a7c627982a22598013206b","source":{"kind":"arxiv","id":"2004.01024","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.01024","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"arxiv_version","alias_value":"2004.01024v1","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01024","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_12","alias_value":"YOZ3RY6FYGKJ","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_16","alias_value":"YOZ3RY6FYGKJHERU","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_8","alias_value":"YOZ3RY6F","created_at":"2026-07-05T00:52:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:YOZ3RY6FYGKJHERUP7ENZMIT2O","target":"record","payload":{"canonical_record":{"source":{"id":"2004.01024","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T17:16:47Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ad45f373821ca22ed7d238775469826a1958fbf02e9f28649c9f441613b33bc4","abstract_canon_sha256":"ceb47847a865cf9a808f7ae00bbf2e99704588a591545942f2dc2d89c8ec3043"},"schema_version":"1.0"},"canonical_sha256":"c3b3b8e3c5c1949392347fc8dcb113d3bfd4e8f497a7c627982a22598013206b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:52:23.674056Z","signature_b64":"quUwJtLPp3l/Djt/p1jFMP2Bccm2UsFdZdDyjXFeE5LmOQbPMKZJohCLrKDSz67mQpRc4l+h8On1BXf0jnE9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3b3b8e3c5c1949392347fc8dcb113d3bfd4e8f497a7c627982a22598013206b","last_reissued_at":"2026-07-05T00:52:23.673647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:52:23.673647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2004.01024","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:52:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qPe8QsAp/OlvUPBeif6dowmpz36jafJRY3JkAeAxqQn+1/eQ8rl6KUeZFzLTIwf4lDfgh+UrbsnlwUJXVF9qDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T19:11:09.580741Z"},"content_sha256":"02cc7d2a459d38ae6d1532bc335ef6d6bf4ccfdcbba1f403f75da14a769bc95f","schema_version":"1.0","event_id":"sha256:02cc7d2a459d38ae6d1532bc335ef6d6bf4ccfdcbba1f403f75da14a769bc95f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:YOZ3RY6FYGKJHERUP7ENZMIT2O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.SI","authors_text":"Hansheng Xue, Luwei Yang, Wen Jiang, Yi Hu, Yi Wei, Yu Lin","submitted_at":"2020-04-01T17:16:47Z","abstract_excerpt":"Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification. Most of existing network embedding algorithms focus on how to learn static homogeneous networks effectively. However, networks in the real world are more complex, e.g., networks may consist of several types of nodes and edges (called heterogeneous information) and may vary over time in terms of dynamic nodes and edges (called evolutionary patterns). Limited wor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01024","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/2004.01024/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:52:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"181VjSHSzcdTUOTw/aJFzz6vSnip63uBGvC9reruDqujyJtBLXqVSWl9bewi12Ln6PJsEsNeJtsrLJR2QNUuBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-19T19:11:09.581128Z"},"content_sha256":"e2a3aaf02f9b8127d453beb87e6a89e73afb7e8bc0d86c49fcb734ccbcb3f0ba","schema_version":"1.0","event_id":"sha256:e2a3aaf02f9b8127d453beb87e6a89e73afb7e8bc0d86c49fcb734ccbcb3f0ba"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/bundle.json","state_url":"https://pith.science/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-19T19:11:09Z","links":{"resolver":"https://pith.science/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O","bundle":"https://pith.science/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/bundle.json","state":"https://pith.science/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YOZ3RY6FYGKJHERUP7ENZMIT2O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:YOZ3RY6FYGKJHERUP7ENZMIT2O","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":"ceb47847a865cf9a808f7ae00bbf2e99704588a591545942f2dc2d89c8ec3043","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T17:16:47Z","title_canon_sha256":"ad45f373821ca22ed7d238775469826a1958fbf02e9f28649c9f441613b33bc4"},"schema_version":"1.0","source":{"id":"2004.01024","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2004.01024","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"arxiv_version","alias_value":"2004.01024v1","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2004.01024","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_12","alias_value":"YOZ3RY6FYGKJ","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_16","alias_value":"YOZ3RY6FYGKJHERU","created_at":"2026-07-05T00:52:23Z"},{"alias_kind":"pith_short_8","alias_value":"YOZ3RY6F","created_at":"2026-07-05T00:52:23Z"}],"graph_snapshots":[{"event_id":"sha256:e2a3aaf02f9b8127d453beb87e6a89e73afb7e8bc0d86c49fcb734ccbcb3f0ba","target":"graph","created_at":"2026-07-05T00:52:23Z","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/2004.01024/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification. Most of existing network embedding algorithms focus on how to learn static homogeneous networks effectively. However, networks in the real world are more complex, e.g., networks may consist of several types of nodes and edges (called heterogeneous information) and may vary over time in terms of dynamic nodes and edges (called evolutionary patterns). Limited wor","authors_text":"Hansheng Xue, Luwei Yang, Wen Jiang, Yi Hu, Yi Wei, Yu Lin","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T17:16:47Z","title":"Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2004.01024","kind":"arxiv","version":1},"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":"sha256:02cc7d2a459d38ae6d1532bc335ef6d6bf4ccfdcbba1f403f75da14a769bc95f","target":"record","created_at":"2026-07-05T00:52:23Z","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":"ceb47847a865cf9a808f7ae00bbf2e99704588a591545942f2dc2d89c8ec3043","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2020-04-01T17:16:47Z","title_canon_sha256":"ad45f373821ca22ed7d238775469826a1958fbf02e9f28649c9f441613b33bc4"},"schema_version":"1.0","source":{"id":"2004.01024","kind":"arxiv","version":1}},"canonical_sha256":"c3b3b8e3c5c1949392347fc8dcb113d3bfd4e8f497a7c627982a22598013206b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c3b3b8e3c5c1949392347fc8dcb113d3bfd4e8f497a7c627982a22598013206b","first_computed_at":"2026-07-05T00:52:23.673647Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:52:23.673647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"quUwJtLPp3l/Djt/p1jFMP2Bccm2UsFdZdDyjXFeE5LmOQbPMKZJohCLrKDSz67mQpRc4l+h8On1BXf0jnE9Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:52:23.674056Z","signed_message":"canonical_sha256_bytes"},"source_id":"2004.01024","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:02cc7d2a459d38ae6d1532bc335ef6d6bf4ccfdcbba1f403f75da14a769bc95f","sha256:e2a3aaf02f9b8127d453beb87e6a89e73afb7e8bc0d86c49fcb734ccbcb3f0ba"],"state_sha256":"4783c640623a8e74666b98ee81d3910fe6070cc83f85b454185d11f654ff4c19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vWk3paLAyX7xuwYgFh9SeUlwViyX8Enpgw6knkFtaxlNd6IpSbk+fBdBQ93da6uVdYoUBmuMr8cMem6qlU9JCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-19T19:11:09.583429Z","bundle_sha256":"0cb96e002e49ab31f73ae96059689eb7c734c971850d0d9e27f1157014fd058a"}}