{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ZOK3XJBNS3N234SYWXSTTXX3VU","short_pith_number":"pith:ZOK3XJBN","canonical_record":{"source":{"id":"2305.19872","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T14:09:42Z","cross_cats_sorted":[],"title_canon_sha256":"e7c4490d8f0982f9cf19d7a7ac807eed23ebf6b85f794625d8e89cc16d786f68","abstract_canon_sha256":"7458a89a469b9de1b6e3cdd8eadbf28bda474af6b649244dce312e474b04ac58"},"schema_version":"1.0"},"canonical_sha256":"cb95bba42d96dbadf258b5e539defbad382fd1e5c3f8a65cbf44b528aa6a079b","source":{"kind":"arxiv","id":"2305.19872","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.19872","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"arxiv_version","alias_value":"2305.19872v3","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19872","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_12","alias_value":"ZOK3XJBNS3N2","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_16","alias_value":"ZOK3XJBNS3N234SY","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_8","alias_value":"ZOK3XJBN","created_at":"2026-07-05T08:16:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ZOK3XJBNS3N234SYWXSTTXX3VU","target":"record","payload":{"canonical_record":{"source":{"id":"2305.19872","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T14:09:42Z","cross_cats_sorted":[],"title_canon_sha256":"e7c4490d8f0982f9cf19d7a7ac807eed23ebf6b85f794625d8e89cc16d786f68","abstract_canon_sha256":"7458a89a469b9de1b6e3cdd8eadbf28bda474af6b649244dce312e474b04ac58"},"schema_version":"1.0"},"canonical_sha256":"cb95bba42d96dbadf258b5e539defbad382fd1e5c3f8a65cbf44b528aa6a079b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:20.881405Z","signature_b64":"GwI72iw8WrIGJX9Vjpk7b7be1TfnOoxf21jIKNAV33vDrvurU1/6FX2RQohj3n9MiXcBxXtS0HDK2nywacUtDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb95bba42d96dbadf258b5e539defbad382fd1e5c3f8a65cbf44b528aa6a079b","last_reissued_at":"2026-07-05T08:16:20.881016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:20.881016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.19872","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":"2026-07-05T08:16:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2mXB3D9AJ32zBiZu3aj85Z+IdRhIixblBWZ+tbTz9uVZJsRKeHdFJyzaSnFFkZ+QQoaQ6eg5GPpH7NRgof0DAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:38:10.801074Z"},"content_sha256":"bc19a5463cfd1110b519cc2fcf9c0283ed93e18430a682217bf5b39ecd2523c4","schema_version":"1.0","event_id":"sha256:bc19a5463cfd1110b519cc2fcf9c0283ed93e18430a682217bf5b39ecd2523c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ZOK3XJBNS3N234SYWXSTTXX3VU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dianhai Yu, Mingguo He, Shikun Feng, Weibin Li, Yu Sun, Zhengjie Huang, Zhewei Wei","submitted_at":"2023-05-31T14:09:42Z","abstract_excerpt":"Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness. To tackle these issues, we present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. Then, using t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19872","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/2305.19872/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":"2026-07-05T08:16:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z5VbGy05I8NM+i64HfjDpIc1tK01DT0SBKosTE7EVZa1Gfw0cLauBhtx+o1HhQrbAKbWCrqUfPkcFmBRHL2lDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:38:10.802287Z"},"content_sha256":"cc18157cbf6335eef00fdb535bc76ccd1572fbfa334170108aa1ec73822ed6a5","schema_version":"1.0","event_id":"sha256:cc18157cbf6335eef00fdb535bc76ccd1572fbfa334170108aa1ec73822ed6a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/bundle.json","state_url":"https://pith.science/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/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":"2026-08-11T03:38:10Z","links":{"resolver":"https://pith.science/pith/ZOK3XJBNS3N234SYWXSTTXX3VU","bundle":"https://pith.science/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/bundle.json","state":"https://pith.science/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZOK3XJBNS3N234SYWXSTTXX3VU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ZOK3XJBNS3N234SYWXSTTXX3VU","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":"7458a89a469b9de1b6e3cdd8eadbf28bda474af6b649244dce312e474b04ac58","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T14:09:42Z","title_canon_sha256":"e7c4490d8f0982f9cf19d7a7ac807eed23ebf6b85f794625d8e89cc16d786f68"},"schema_version":"1.0","source":{"id":"2305.19872","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.19872","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"arxiv_version","alias_value":"2305.19872v3","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19872","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_12","alias_value":"ZOK3XJBNS3N2","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_16","alias_value":"ZOK3XJBNS3N234SY","created_at":"2026-07-05T08:16:20Z"},{"alias_kind":"pith_short_8","alias_value":"ZOK3XJBN","created_at":"2026-07-05T08:16:20Z"}],"graph_snapshots":[{"event_id":"sha256:cc18157cbf6335eef00fdb535bc76ccd1572fbfa334170108aa1ec73822ed6a5","target":"graph","created_at":"2026-07-05T08:16:20Z","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/2305.19872/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness. To tackle these issues, we present a positive spectral heterogeneous graph convolution via positive noncommutative polynomials. Then, using t","authors_text":"Dianhai Yu, Mingguo He, Shikun Feng, Weibin Li, Yu Sun, Zhengjie Huang, Zhewei Wei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T14:09:42Z","title":"Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19872","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:bc19a5463cfd1110b519cc2fcf9c0283ed93e18430a682217bf5b39ecd2523c4","target":"record","created_at":"2026-07-05T08:16:20Z","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":"7458a89a469b9de1b6e3cdd8eadbf28bda474af6b649244dce312e474b04ac58","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-31T14:09:42Z","title_canon_sha256":"e7c4490d8f0982f9cf19d7a7ac807eed23ebf6b85f794625d8e89cc16d786f68"},"schema_version":"1.0","source":{"id":"2305.19872","kind":"arxiv","version":3}},"canonical_sha256":"cb95bba42d96dbadf258b5e539defbad382fd1e5c3f8a65cbf44b528aa6a079b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cb95bba42d96dbadf258b5e539defbad382fd1e5c3f8a65cbf44b528aa6a079b","first_computed_at":"2026-07-05T08:16:20.881016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:16:20.881016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GwI72iw8WrIGJX9Vjpk7b7be1TfnOoxf21jIKNAV33vDrvurU1/6FX2RQohj3n9MiXcBxXtS0HDK2nywacUtDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:16:20.881405Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.19872","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bc19a5463cfd1110b519cc2fcf9c0283ed93e18430a682217bf5b39ecd2523c4","sha256:cc18157cbf6335eef00fdb535bc76ccd1572fbfa334170108aa1ec73822ed6a5"],"state_sha256":"62c5c3bdf43df02cdddde3867d4c0840f87001a2f96c34f506995aa39ca1d312"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EJl3jAKsuRVR6H67dISucotlAAuW8EGADKy5nBSy3/dIq21FgUSTNqnOMqVb8RbDi4UDfAeSNTYIDZZ5YPM2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T03:38:10.823257Z","bundle_sha256":"1141fdd7ae5588df209c3cde74a072cf5e067994d836fcb3af8601fbffda086e"}}