{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:TUPMPOBJFZSJACZY23FRZ2QQ55","short_pith_number":"pith:TUPMPOBJ","canonical_record":{"source":{"id":"2103.07877","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-14T09:25:36Z","cross_cats_sorted":[],"title_canon_sha256":"91e50ebe48cd393b7b3feb096f783f1436e92e27ef511ca6ddd39b23364d8524","abstract_canon_sha256":"4d46c7a9a75d8ec83974f49922e377d424cb68008741a3ec2296da58fa70ade5"},"schema_version":"1.0"},"canonical_sha256":"9d1ec7b8292e64900b38d6cb1cea10ef4a65c96dce6da82634ba553d98377ff6","source":{"kind":"arxiv","id":"2103.07877","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.07877","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"2103.07877v3","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.07877","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"TUPMPOBJFZSJ","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_16","alias_value":"TUPMPOBJFZSJACZY","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_8","alias_value":"TUPMPOBJ","created_at":"2026-07-05T02:52:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:TUPMPOBJFZSJACZY23FRZ2QQ55","target":"record","payload":{"canonical_record":{"source":{"id":"2103.07877","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-14T09:25:36Z","cross_cats_sorted":[],"title_canon_sha256":"91e50ebe48cd393b7b3feb096f783f1436e92e27ef511ca6ddd39b23364d8524","abstract_canon_sha256":"4d46c7a9a75d8ec83974f49922e377d424cb68008741a3ec2296da58fa70ade5"},"schema_version":"1.0"},"canonical_sha256":"9d1ec7b8292e64900b38d6cb1cea10ef4a65c96dce6da82634ba553d98377ff6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:52:15.542582Z","signature_b64":"ad8bwrbL3+AQrUDdnXjVZ7jthRtw8Qj2sr3bPtj4i0QLWzjEX2CocEjyn78df7q2OqlPKexiH98eTrT4QO0xBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d1ec7b8292e64900b38d6cb1cea10ef4a65c96dce6da82634ba553d98377ff6","last_reissued_at":"2026-07-05T02:52:15.542228Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:52:15.542228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.07877","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RzeqFHlGmYm/0mWCCEzHhT7VlITXVcGPCviMNUgt/IA/TzTwwMhakD/+bKW8oni3D3220t/IvUuufY6YM6FwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T20:43:42.856405Z"},"content_sha256":"255c9c21a9ed726790b6bd39118e63d434737041516b0acd5a82e3e963f9adb1","schema_version":"1.0","event_id":"sha256:255c9c21a9ed726790b6bd39118e63d434737041516b0acd5a82e3e963f9adb1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:TUPMPOBJFZSJACZY23FRZ2QQ55","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Guizhong Liu, Mengying Jiang, Xinliang Wu","submitted_at":"2021-03-14T09:25:36Z","abstract_excerpt":"Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need meta-path, which is much improve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.07877","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/2103.07877/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":"2026-07-05T02:52:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YTMC0RWgRboPmqF6zBjhdfA3bTBTwnZLgxSMo0QMDhqvGimXyyeGHUtw1LkH+pBNwVQh6O5up63uSzHd99jhBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T20:43:42.856911Z"},"content_sha256":"3ea623d609e32dabf71cdc740a3f2a0b8033c0aff49f35a742291d41dc19e1d7","schema_version":"1.0","event_id":"sha256:3ea623d609e32dabf71cdc740a3f2a0b8033c0aff49f35a742291d41dc19e1d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/bundle.json","state_url":"https://pith.science/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/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":"2026-08-20T20:43:42Z","links":{"resolver":"https://pith.science/pith/TUPMPOBJFZSJACZY23FRZ2QQ55","bundle":"https://pith.science/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/bundle.json","state":"https://pith.science/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TUPMPOBJFZSJACZY23FRZ2QQ55/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:TUPMPOBJFZSJACZY23FRZ2QQ55","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":"4d46c7a9a75d8ec83974f49922e377d424cb68008741a3ec2296da58fa70ade5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-14T09:25:36Z","title_canon_sha256":"91e50ebe48cd393b7b3feb096f783f1436e92e27ef511ca6ddd39b23364d8524"},"schema_version":"1.0","source":{"id":"2103.07877","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.07877","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"arxiv_version","alias_value":"2103.07877v3","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.07877","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_12","alias_value":"TUPMPOBJFZSJ","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_16","alias_value":"TUPMPOBJFZSJACZY","created_at":"2026-07-05T02:52:15Z"},{"alias_kind":"pith_short_8","alias_value":"TUPMPOBJ","created_at":"2026-07-05T02:52:15Z"}],"graph_snapshots":[{"event_id":"sha256:3ea623d609e32dabf71cdc740a3f2a0b8033c0aff49f35a742291d41dc19e1d7","target":"graph","created_at":"2026-07-05T02:52:15Z","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/2103.07877/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need meta-path, which is much improve","authors_text":"Guizhong Liu, Mengying Jiang, Xinliang Wu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-14T09:25:36Z","title":"R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.07877","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":"sha256:255c9c21a9ed726790b6bd39118e63d434737041516b0acd5a82e3e963f9adb1","target":"record","created_at":"2026-07-05T02:52:15Z","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":"4d46c7a9a75d8ec83974f49922e377d424cb68008741a3ec2296da58fa70ade5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2021-03-14T09:25:36Z","title_canon_sha256":"91e50ebe48cd393b7b3feb096f783f1436e92e27ef511ca6ddd39b23364d8524"},"schema_version":"1.0","source":{"id":"2103.07877","kind":"arxiv","version":3}},"canonical_sha256":"9d1ec7b8292e64900b38d6cb1cea10ef4a65c96dce6da82634ba553d98377ff6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d1ec7b8292e64900b38d6cb1cea10ef4a65c96dce6da82634ba553d98377ff6","first_computed_at":"2026-07-05T02:52:15.542228Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:52:15.542228Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ad8bwrbL3+AQrUDdnXjVZ7jthRtw8Qj2sr3bPtj4i0QLWzjEX2CocEjyn78df7q2OqlPKexiH98eTrT4QO0xBA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:52:15.542582Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.07877","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:255c9c21a9ed726790b6bd39118e63d434737041516b0acd5a82e3e963f9adb1","sha256:3ea623d609e32dabf71cdc740a3f2a0b8033c0aff49f35a742291d41dc19e1d7"],"state_sha256":"c7aa5c342e87cf72f2d7ae21ece95589c4d28501d517e33a64e06f615ef9d07c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V9K1Lw306XIA6Pfa8yW2aMA73unRJxcJ3MEijVlc6a1dMaHng5tAAjErXuYk5uSZcXa4X2f+GyTbhddYUM8JAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T20:43:42.861734Z","bundle_sha256":"a66d346c231df8165b2ffc4ad06d1a55e574f30b49b823608cd68e9fafb6bd4f"}}