{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:OX5M5IKWHQ3NZQ2VO6YYQJQRBR","short_pith_number":"pith:OX5M5IKW","canonical_record":{"source":{"id":"2104.01711","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2021-04-04T23:31:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4d130ed6d9b298b9512823c65f121f7af0e6bf405d8237a707aae4966283acd0","abstract_canon_sha256":"4111e46c615b22fb8d794d819ed6c7a2c995dc5692d048f64efea892231bdfe2"},"schema_version":"1.0"},"canonical_sha256":"75facea1563c36dcc35577b18826110c511f3e88fd137024bbe4eb9407815689","source":{"kind":"arxiv","id":"2104.01711","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.01711","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"arxiv_version","alias_value":"2104.01711v2","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01711","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_12","alias_value":"OX5M5IKWHQ3N","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"OX5M5IKWHQ3NZQ2V","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"OX5M5IKW","created_at":"2026-07-05T02:31:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:OX5M5IKWHQ3NZQ2VO6YYQJQRBR","target":"record","payload":{"canonical_record":{"source":{"id":"2104.01711","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2021-04-04T23:31:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4d130ed6d9b298b9512823c65f121f7af0e6bf405d8237a707aae4966283acd0","abstract_canon_sha256":"4111e46c615b22fb8d794d819ed6c7a2c995dc5692d048f64efea892231bdfe2"},"schema_version":"1.0"},"canonical_sha256":"75facea1563c36dcc35577b18826110c511f3e88fd137024bbe4eb9407815689","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:31:05.333917Z","signature_b64":"1/4DymPo9zOu9i2vIbtx1YDEoxgJEqNNm8oqDroKIEOTDbtG5vher9bN+LSgYiiMC3QQiZBOcPhCBqpKyjU4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75facea1563c36dcc35577b18826110c511f3e88fd137024bbe4eb9407815689","last_reissued_at":"2026-07-05T02:31:05.333400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:31:05.333400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.01711","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:31:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zT7nf0QWMkzaL36PWxPlplgFtKYbkj7KdWX7jdohDqmtJeIt9fqA3Ozbzhk8oj7VAVHS4OQp9KyA1FYaBhw9Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:07:03.153837Z"},"content_sha256":"044a958f72728f7e116acd51e781ec28010ba0bd4aa9fca54196d4c5e4dcfb02","schema_version":"1.0","event_id":"sha256:044a958f72728f7e116acd51e781ec28010ba0bd4aa9fca54196d4c5e4dcfb02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:OX5M5IKWHQ3NZQ2VO6YYQJQRBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Hao Wang, Hongzhi Yin, Jie Ren, Tong Chen, Xiangliang Zhang, Zi Huang","submitted_at":"2021-04-04T23:31:39Z","abstract_excerpt":"With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, despite the necessity of inductively producing representations for comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01711","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/2104.01711/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":"2026-07-05T02:31:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5VJwcPFXqTFa5m+S9v/f6WtovvnXziI3OVIyLJnQwELLGGLomYrB9fmp+ASosaoatFObD4ShNQDr8G+NuFzBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T23:07:03.154415Z"},"content_sha256":"d0db0260ede04187c018822d6b17c50e53bbfefa28ff2b6bee16aa7551c78386","schema_version":"1.0","event_id":"sha256:d0db0260ede04187c018822d6b17c50e53bbfefa28ff2b6bee16aa7551c78386"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/bundle.json","state_url":"https://pith.science/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/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":"2026-08-04T23:07:03Z","links":{"resolver":"https://pith.science/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR","bundle":"https://pith.science/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/bundle.json","state":"https://pith.science/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OX5M5IKWHQ3NZQ2VO6YYQJQRBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:OX5M5IKWHQ3NZQ2VO6YYQJQRBR","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":"4111e46c615b22fb8d794d819ed6c7a2c995dc5692d048f64efea892231bdfe2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2021-04-04T23:31:39Z","title_canon_sha256":"4d130ed6d9b298b9512823c65f121f7af0e6bf405d8237a707aae4966283acd0"},"schema_version":"1.0","source":{"id":"2104.01711","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.01711","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"arxiv_version","alias_value":"2104.01711v2","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.01711","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_12","alias_value":"OX5M5IKWHQ3N","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_16","alias_value":"OX5M5IKWHQ3NZQ2V","created_at":"2026-07-05T02:31:05Z"},{"alias_kind":"pith_short_8","alias_value":"OX5M5IKW","created_at":"2026-07-05T02:31:05Z"}],"graph_snapshots":[{"event_id":"sha256:d0db0260ede04187c018822d6b17c50e53bbfefa28ff2b6bee16aa7551c78386","target":"graph","created_at":"2026-07-05T02:31:05Z","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/2104.01711/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, despite the necessity of inductively producing representations for comp","authors_text":"Hao Wang, Hongzhi Yin, Jie Ren, Tong Chen, Xiangliang Zhang, Zi Huang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2021-04-04T23:31:39Z","title":"Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.01711","kind":"arxiv","version":2},"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":"sha256:044a958f72728f7e116acd51e781ec28010ba0bd4aa9fca54196d4c5e4dcfb02","target":"record","created_at":"2026-07-05T02:31:05Z","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":"4111e46c615b22fb8d794d819ed6c7a2c995dc5692d048f64efea892231bdfe2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2021-04-04T23:31:39Z","title_canon_sha256":"4d130ed6d9b298b9512823c65f121f7af0e6bf405d8237a707aae4966283acd0"},"schema_version":"1.0","source":{"id":"2104.01711","kind":"arxiv","version":2}},"canonical_sha256":"75facea1563c36dcc35577b18826110c511f3e88fd137024bbe4eb9407815689","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75facea1563c36dcc35577b18826110c511f3e88fd137024bbe4eb9407815689","first_computed_at":"2026-07-05T02:31:05.333400Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:31:05.333400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1/4DymPo9zOu9i2vIbtx1YDEoxgJEqNNm8oqDroKIEOTDbtG5vher9bN+LSgYiiMC3QQiZBOcPhCBqpKyjU4Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:31:05.333917Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.01711","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:044a958f72728f7e116acd51e781ec28010ba0bd4aa9fca54196d4c5e4dcfb02","sha256:d0db0260ede04187c018822d6b17c50e53bbfefa28ff2b6bee16aa7551c78386"],"state_sha256":"94fc0ae520af8dfca5ac9045c81310a876aafc1060896d4b3a0bb7e1bc3cd3d4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5SUPBQr0yj5KM2cq9S5fJfLirXNz0eSbIMtLhu4FdUZX6FPkVraxfXMnTj5kZhrvmz+LFCVZ3ghjmMR7pveaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T23:07:03.157745Z","bundle_sha256":"e1063c5e31408271474f8ace882e4a6e60927afb1cf8fbead14aa802c740a483"}}