{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MJGOQYYAARSY2HE2KKEUM2G44G","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":"c3205ec9373fe7ba7b9411c5eec798aaa7a7f9f8973c7886caf1faa783a56a62","cross_cats_sorted":["cs.AI","cs.CV","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-27T19:09:21Z","title_canon_sha256":"88d445aba81845b14dd10e4b5e3eb5690d0af8e30fe02b31d512a18f99566866"},"schema_version":"1.0","source":{"id":"2202.13427","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.13427","created_at":"2026-07-05T06:36:51Z"},{"alias_kind":"arxiv_version","alias_value":"2202.13427v1","created_at":"2026-07-05T06:36:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.13427","created_at":"2026-07-05T06:36:51Z"},{"alias_kind":"pith_short_12","alias_value":"MJGOQYYAARSY","created_at":"2026-07-05T06:36:51Z"},{"alias_kind":"pith_short_16","alias_value":"MJGOQYYAARSY2HE2","created_at":"2026-07-05T06:36:51Z"},{"alias_kind":"pith_short_8","alias_value":"MJGOQYYA","created_at":"2026-07-05T06:36:51Z"}],"graph_snapshots":[{"event_id":"sha256:eed0d1907323dbb840dd43f88272b87c9a9a86fbb6657281c4c8181dd08df2d0","target":"graph","created_at":"2026-07-05T06:36:51Z","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/2202.13427/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatio-temporal graphs (ST-graphs) have been used to model time series tasks such as traffic forecasting, human motion modeling, and action recognition. The high-level structure and corresponding features from ST-graphs have led to improved performance over traditional architectures. However, current methods tend to be limited by simple features, despite the rich information provided by the full graph structure, which leads to inefficiencies and suboptimal performance in downstream tasks. We propose the use of features derived from meta-paths, walks across different types of edges, in ST-graph","authors_text":"Aamir Hasan, Katherine Driggs-Campbell, Pranav Sriram","cross_cats":["cs.AI","cs.CV","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-27T19:09:21Z","title":"Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.13427","kind":"arxiv","version":1},"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":"sha256:3e698b1a5da314772958ebfead61cc78e92dc88a088319ab229ff2e07daa6bc2","target":"record","created_at":"2026-07-05T06:36:51Z","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":"c3205ec9373fe7ba7b9411c5eec798aaa7a7f9f8973c7886caf1faa783a56a62","cross_cats_sorted":["cs.AI","cs.CV","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-27T19:09:21Z","title_canon_sha256":"88d445aba81845b14dd10e4b5e3eb5690d0af8e30fe02b31d512a18f99566866"},"schema_version":"1.0","source":{"id":"2202.13427","kind":"arxiv","version":1}},"canonical_sha256":"624ce8630004658d1c9a52894668dce18e1aae183daa1da91f1275bafd81d07f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"624ce8630004658d1c9a52894668dce18e1aae183daa1da91f1275bafd81d07f","first_computed_at":"2026-07-05T06:36:51.692669Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:36:51.692669Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8dDnlaQTJsevxVAREE2FUhZnHa8fA4FrCkR61P4Tyd1JQ7rmW3Ayq4D1Gf1lmEhpXArv2ytIU2Gsd8ap6VtsCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:36:51.693091Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.13427","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e698b1a5da314772958ebfead61cc78e92dc88a088319ab229ff2e07daa6bc2","sha256:eed0d1907323dbb840dd43f88272b87c9a9a86fbb6657281c4c8181dd08df2d0"],"state_sha256":"3c1c9a9c36534a4e04bb4eafff55229dde5bcb073e34cef721139088f2131fb0"}