{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:Y5M4H7CTK3V4A6EPY2EDINCO3D","short_pith_number":"pith:Y5M4H7CT","canonical_record":{"source":{"id":"2410.10915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T07:33:33Z","cross_cats_sorted":[],"title_canon_sha256":"b204f71f257175930750ac69f27777a64d3f52c43b917a0f24c5099c676f0744","abstract_canon_sha256":"3345c506bc13666f5b0762189031983d8456a5aa5f3152d0d84ddaa579022303"},"schema_version":"1.0"},"canonical_sha256":"c759c3fc5356ebc0788fc68834344ed8e68bacf5317863151a8df133a3a7e218","source":{"kind":"arxiv","id":"2410.10915","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.10915","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"arxiv_version","alias_value":"2410.10915v2","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10915","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_12","alias_value":"Y5M4H7CTK3V4","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_16","alias_value":"Y5M4H7CTK3V4A6EP","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_8","alias_value":"Y5M4H7CT","created_at":"2026-07-05T11:53:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:Y5M4H7CTK3V4A6EPY2EDINCO3D","target":"record","payload":{"canonical_record":{"source":{"id":"2410.10915","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T07:33:33Z","cross_cats_sorted":[],"title_canon_sha256":"b204f71f257175930750ac69f27777a64d3f52c43b917a0f24c5099c676f0744","abstract_canon_sha256":"3345c506bc13666f5b0762189031983d8456a5aa5f3152d0d84ddaa579022303"},"schema_version":"1.0"},"canonical_sha256":"c759c3fc5356ebc0788fc68834344ed8e68bacf5317863151a8df133a3a7e218","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:53:42.067604Z","signature_b64":"3vU37b77RIj38kjsrdzXWWXSUtt+e6RQ2dLDentFBHpha56N6UzuMJO/5ezNFOF8nsFHMalXCtOKocmRfdFzBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c759c3fc5356ebc0788fc68834344ed8e68bacf5317863151a8df133a3a7e218","last_reissued_at":"2026-07-05T11:53:42.067109Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:53:42.067109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.10915","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":"2026-07-05T11:53:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hW5V74F203M3u6TV5hSSWC/ImjxWTQVYhOaVnONOqtKtHBkcvJyxtJJnHbhHxjyEWoPI3hZr9GDKDtpHAdFhCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:36:00.453489Z"},"content_sha256":"66e4b49b8a63fb38c443192cd93c27ba8b4094e8af2e36e5a376b565b6aa7557","schema_version":"1.0","event_id":"sha256:66e4b49b8a63fb38c443192cd93c27ba8b4094e8af2e36e5a376b565b6aa7557"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:Y5M4H7CTK3V4A6EPY2EDINCO3D","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Dong Huang, Haixin Wang, Hongzhi Yin, Qianru Zhang, Siu-Ming Yiu, Xinyi Gao","submitted_at":"2024-10-14T07:33:33Z","abstract_excerpt":"Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, a key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks' ability to learn meaningful region representations in the spatial-temporal graph. To overcome these limitations, we propose HGAurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10915","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/2410.10915/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":"2026-07-05T11:53:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yglLeMGlcgunGRASBbBb714RcEU+pHqQBZkzfUOgzjC1klc0qn6CEQlDtXu6vYFJQtr35SvGffht2Ws/Fch6BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T21:36:00.454016Z"},"content_sha256":"594d57ad1e7eb40844fdf24362f4bc1833ca57241877617748f955b9eb2b5e7b","schema_version":"1.0","event_id":"sha256:594d57ad1e7eb40844fdf24362f4bc1833ca57241877617748f955b9eb2b5e7b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/bundle.json","state_url":"https://pith.science/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/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":"2026-08-04T21:36:00Z","links":{"resolver":"https://pith.science/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D","bundle":"https://pith.science/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/bundle.json","state":"https://pith.science/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y5M4H7CTK3V4A6EPY2EDINCO3D/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:Y5M4H7CTK3V4A6EPY2EDINCO3D","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":"3345c506bc13666f5b0762189031983d8456a5aa5f3152d0d84ddaa579022303","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T07:33:33Z","title_canon_sha256":"b204f71f257175930750ac69f27777a64d3f52c43b917a0f24c5099c676f0744"},"schema_version":"1.0","source":{"id":"2410.10915","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.10915","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"arxiv_version","alias_value":"2410.10915v2","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10915","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_12","alias_value":"Y5M4H7CTK3V4","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_16","alias_value":"Y5M4H7CTK3V4A6EP","created_at":"2026-07-05T11:53:42Z"},{"alias_kind":"pith_short_8","alias_value":"Y5M4H7CT","created_at":"2026-07-05T11:53:42Z"}],"graph_snapshots":[{"event_id":"sha256:594d57ad1e7eb40844fdf24362f4bc1833ca57241877617748f955b9eb2b5e7b","target":"graph","created_at":"2026-07-05T11:53:42Z","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/2410.10915/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatial-temporal graph representations play a crucial role in urban sensing applications, including traffic analysis, human mobility behavior modeling, and citywide crime prediction. However, a key challenge lies in the noisy and sparse nature of spatial-temporal data, which limits existing neural networks' ability to learn meaningful region representations in the spatial-temporal graph. To overcome these limitations, we propose HGAurban, a novel heterogeneous spatial-temporal graph masked autoencoder that leverages generative self-supervised learning for robust urban data representation. Our ","authors_text":"Dong Huang, Haixin Wang, Hongzhi Yin, Qianru Zhang, Siu-Ming Yiu, Xinyi Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T07:33:33Z","title":"HGAurban: Heterogeneous Graph Autoencoding for Urban Spatial-Temporal Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10915","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":"sha256:66e4b49b8a63fb38c443192cd93c27ba8b4094e8af2e36e5a376b565b6aa7557","target":"record","created_at":"2026-07-05T11:53:42Z","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":"3345c506bc13666f5b0762189031983d8456a5aa5f3152d0d84ddaa579022303","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-14T07:33:33Z","title_canon_sha256":"b204f71f257175930750ac69f27777a64d3f52c43b917a0f24c5099c676f0744"},"schema_version":"1.0","source":{"id":"2410.10915","kind":"arxiv","version":2}},"canonical_sha256":"c759c3fc5356ebc0788fc68834344ed8e68bacf5317863151a8df133a3a7e218","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c759c3fc5356ebc0788fc68834344ed8e68bacf5317863151a8df133a3a7e218","first_computed_at":"2026-07-05T11:53:42.067109Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:53:42.067109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3vU37b77RIj38kjsrdzXWWXSUtt+e6RQ2dLDentFBHpha56N6UzuMJO/5ezNFOF8nsFHMalXCtOKocmRfdFzBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:53:42.067604Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.10915","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:66e4b49b8a63fb38c443192cd93c27ba8b4094e8af2e36e5a376b565b6aa7557","sha256:594d57ad1e7eb40844fdf24362f4bc1833ca57241877617748f955b9eb2b5e7b"],"state_sha256":"e2db1f82a9dcf7ef39d30f66f5cdc1bbcb4a169d06fdbff4a4cc95e9589715d3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O1mPqczEPn9pp3MAdtq8u1tmZcrWyOeNCnQ92NkCBNu+ZhQGkyuck+L2LeTwuiwxstDIJmW/iTvKd0z/8WhiCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T21:36:00.460024Z","bundle_sha256":"846aa2db04fcf830f0766ed0a435f41579c66dccb3923c7609c560215958a490"}}