{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:TWQOCC6XL22EYCYDVHXT5A7LWJ","short_pith_number":"pith:TWQOCC6X","canonical_record":{"source":{"id":"2301.10569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-25T13:17:46Z","cross_cats_sorted":[],"title_canon_sha256":"e907baa61a227ab75a08bfab1d98d4c14d4b2ca22b4ec8561867df2eaa79bdfc","abstract_canon_sha256":"bf459e56570e3206eaf4ab56e149217fb3de436291f36685c08207d20cfded46"},"schema_version":"1.0"},"canonical_sha256":"9da0e10bd75eb44c0b03a9ef3e83ebb242eeb4b2f15ea493ccd5063e535710a4","source":{"kind":"arxiv","id":"2301.10569","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.10569","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"arxiv_version","alias_value":"2301.10569v2","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10569","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_12","alias_value":"TWQOCC6XL22E","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_16","alias_value":"TWQOCC6XL22EYCYD","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_8","alias_value":"TWQOCC6X","created_at":"2026-07-05T05:40:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:TWQOCC6XL22EYCYDVHXT5A7LWJ","target":"record","payload":{"canonical_record":{"source":{"id":"2301.10569","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-25T13:17:46Z","cross_cats_sorted":[],"title_canon_sha256":"e907baa61a227ab75a08bfab1d98d4c14d4b2ca22b4ec8561867df2eaa79bdfc","abstract_canon_sha256":"bf459e56570e3206eaf4ab56e149217fb3de436291f36685c08207d20cfded46"},"schema_version":"1.0"},"canonical_sha256":"9da0e10bd75eb44c0b03a9ef3e83ebb242eeb4b2f15ea493ccd5063e535710a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:40:52.889885Z","signature_b64":"QazT+Y1ut22a3NSWoM+o/6ldiYDFmMOxJ/wzYtJnorkHNMPSBBJPVWs7FN6ojBodMBF9ujWGBGgm8c7SEAZ9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9da0e10bd75eb44c0b03a9ef3e83ebb242eeb4b2f15ea493ccd5063e535710a4","last_reissued_at":"2026-07-05T05:40:52.889424Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:40:52.889424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.10569","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":"2026-07-05T05:40:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CPa7Mb7SloKjuF4qf43CDT6v6gd21YmsdAiVKWT7RqAbDuZCBHLI7F0djy7yfhdE1Dt4uN8NvXslqmA5onwuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:08:41.652381Z"},"content_sha256":"87f1ed58683fcf01b1bcfc9e3d68f9c3d5ff85366b4534bdab6e38c99886d1d4","schema_version":"1.0","event_id":"sha256:87f1ed58683fcf01b1bcfc9e3d68f9c3d5ff85366b4534bdab6e38c99886d1d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:TWQOCC6XL22EYCYDVHXT5A7LWJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatio-Temporal Graph Neural Networks: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Mariette Awad, Zahraa Al Sahili","submitted_at":"2023-01-25T13:17:46Z","abstract_excerpt":"Graph Neural Networks have gained huge interest in the past few years. These powerful algorithms expanded deep learning models to non-Euclidean space and were able to achieve state of art performance in various applications including recommender systems and social networks. However, this performance is based on static graph structures assumption which limits the Graph Neural Networks performance when the data varies with time. Spatiotemporal Graph Neural Networks are extension of Graph Neural Networks that takes the time factor into account. Recently, various Spatiotemporal Graph Neural Networ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10569","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/2301.10569/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":"2026-07-05T05:40:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BzDrcGXYC0ZR/9Q9JfSDGfGKWc/SdjdHfrzj9y/abTleAGtqvf676CAePI/RGXYDCMwddb6MWqdBUEHObcPLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T04:08:41.653062Z"},"content_sha256":"c066e9ae0af13e8e14dd1c1861c417fb400d1fa3ccfa026f8627082b543438dc","schema_version":"1.0","event_id":"sha256:c066e9ae0af13e8e14dd1c1861c417fb400d1fa3ccfa026f8627082b543438dc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/bundle.json","state_url":"https://pith.science/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/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":"2026-08-07T04:08:41Z","links":{"resolver":"https://pith.science/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ","bundle":"https://pith.science/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/bundle.json","state":"https://pith.science/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TWQOCC6XL22EYCYDVHXT5A7LWJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TWQOCC6XL22EYCYDVHXT5A7LWJ","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":"bf459e56570e3206eaf4ab56e149217fb3de436291f36685c08207d20cfded46","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-25T13:17:46Z","title_canon_sha256":"e907baa61a227ab75a08bfab1d98d4c14d4b2ca22b4ec8561867df2eaa79bdfc"},"schema_version":"1.0","source":{"id":"2301.10569","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.10569","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"arxiv_version","alias_value":"2301.10569v2","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.10569","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_12","alias_value":"TWQOCC6XL22E","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_16","alias_value":"TWQOCC6XL22EYCYD","created_at":"2026-07-05T05:40:52Z"},{"alias_kind":"pith_short_8","alias_value":"TWQOCC6X","created_at":"2026-07-05T05:40:52Z"}],"graph_snapshots":[{"event_id":"sha256:c066e9ae0af13e8e14dd1c1861c417fb400d1fa3ccfa026f8627082b543438dc","target":"graph","created_at":"2026-07-05T05:40:52Z","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/2301.10569/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks have gained huge interest in the past few years. These powerful algorithms expanded deep learning models to non-Euclidean space and were able to achieve state of art performance in various applications including recommender systems and social networks. However, this performance is based on static graph structures assumption which limits the Graph Neural Networks performance when the data varies with time. Spatiotemporal Graph Neural Networks are extension of Graph Neural Networks that takes the time factor into account. Recently, various Spatiotemporal Graph Neural Networ","authors_text":"Mariette Awad, Zahraa Al Sahili","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-25T13:17:46Z","title":"Spatio-Temporal Graph Neural Networks: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.10569","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":"sha256:87f1ed58683fcf01b1bcfc9e3d68f9c3d5ff85366b4534bdab6e38c99886d1d4","target":"record","created_at":"2026-07-05T05:40:52Z","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":"bf459e56570e3206eaf4ab56e149217fb3de436291f36685c08207d20cfded46","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-01-25T13:17:46Z","title_canon_sha256":"e907baa61a227ab75a08bfab1d98d4c14d4b2ca22b4ec8561867df2eaa79bdfc"},"schema_version":"1.0","source":{"id":"2301.10569","kind":"arxiv","version":2}},"canonical_sha256":"9da0e10bd75eb44c0b03a9ef3e83ebb242eeb4b2f15ea493ccd5063e535710a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9da0e10bd75eb44c0b03a9ef3e83ebb242eeb4b2f15ea493ccd5063e535710a4","first_computed_at":"2026-07-05T05:40:52.889424Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:40:52.889424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QazT+Y1ut22a3NSWoM+o/6ldiYDFmMOxJ/wzYtJnorkHNMPSBBJPVWs7FN6ojBodMBF9ujWGBGgm8c7SEAZ9Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:40:52.889885Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.10569","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87f1ed58683fcf01b1bcfc9e3d68f9c3d5ff85366b4534bdab6e38c99886d1d4","sha256:c066e9ae0af13e8e14dd1c1861c417fb400d1fa3ccfa026f8627082b543438dc"],"state_sha256":"1b4690efa522adb11cc935b7f4f899dcde28f1fe6e9f329a49a19167e6e59af4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fxeaZAA5sppo30L80u8sxm9NtZWkxevyyVKcS2qE25/XYLf2HX0X8mhkGcxX6tv4v+QR4puXFjkY7zBV2M+SCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T04:08:41.658387Z","bundle_sha256":"c9e87eb75a905671993a6f33425e9895aef67194eb7675d89ebd566b5e6683ed"}}