{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YBNQ5GSJE7B5WAODRJNSETTEZG","short_pith_number":"pith:YBNQ5GSJ","canonical_record":{"source":{"id":"2407.02074","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-02T09:07:19Z","cross_cats_sorted":[],"title_canon_sha256":"723265c0eead864cc72c8c0ed1b32d78231269fa237d0408dbbd6267e8dd9781","abstract_canon_sha256":"4610eccdcefe680e9ddaccb8a007ef80c97af5825142b237d481f3dbc0be8715"},"schema_version":"1.0"},"canonical_sha256":"c05b0e9a4927c3db01c38a5b224e64c9b703ac485ad611c179018e5f07d74262","source":{"kind":"arxiv","id":"2407.02074","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02074","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02074v1","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02074","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_12","alias_value":"YBNQ5GSJE7B5","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_16","alias_value":"YBNQ5GSJE7B5WAOD","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_8","alias_value":"YBNQ5GSJ","created_at":"2026-07-05T08:39:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YBNQ5GSJE7B5WAODRJNSETTEZG","target":"record","payload":{"canonical_record":{"source":{"id":"2407.02074","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-02T09:07:19Z","cross_cats_sorted":[],"title_canon_sha256":"723265c0eead864cc72c8c0ed1b32d78231269fa237d0408dbbd6267e8dd9781","abstract_canon_sha256":"4610eccdcefe680e9ddaccb8a007ef80c97af5825142b237d481f3dbc0be8715"},"schema_version":"1.0"},"canonical_sha256":"c05b0e9a4927c3db01c38a5b224e64c9b703ac485ad611c179018e5f07d74262","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:39:09.710280Z","signature_b64":"gC6ZCPw8XsVpVp38mbV9/ti4Yhs2o861ItK43KZew6V7lPWRP9YS33B6x52dBYXj8Y1NpOXCfpql7kmURg6aBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c05b0e9a4927c3db01c38a5b224e64c9b703ac485ad611c179018e5f07d74262","last_reissued_at":"2026-07-05T08:39:09.709802Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:39:09.709802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.02074","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:39:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MhW+ap7Ft66g6O7JQsrZsE+6eEdPKaIkww5nVXPQ2dbzIA06AGlYvI3Xjit8Ny0VXsFeUEJ9lG7qKFPntXdrAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:48:47.871535Z"},"content_sha256":"dd5a94daf64a90185e93523f47e0022d43cd2e271c741a24c407739004649898","schema_version":"1.0","event_id":"sha256:dd5a94daf64a90185e93523f47e0022d43cd2e271c741a24c407739004649898"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YBNQ5GSJE7B5WAODRJNSETTEZG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CGAP: Urban Region Representation Learning with Coarsened Graph Attention Pooling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Xiao Zhou, Zhuo Xu","submitted_at":"2024-07-02T09:07:19Z","abstract_excerpt":"The explosion of massive urban data recently has provided us with a valuable opportunity to gain deeper insights into urban regions and the daily lives of residents. Urban region representation learning emerges as a crucial realm for fulfilling this task. Among deep learning approaches, graph neural networks (GNNs) have shown promise, given that city elements can be naturally represented as nodes with various connections between them as edges. However, many existing GNN approaches encounter challenges such as over-smoothing and limitations in capturing information from nodes in other regions, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02074","kind":"arxiv","version":1},"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/2407.02074/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":"2026-07-05T08:39:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"prbGqK9MYSKNgGFp36VIybPfpedE7kBg3q6OQ1LzCagvg1qrNNTz5cJZGPKExXxpg5PM2iSw6x828KBHeN6pDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T06:48:47.872283Z"},"content_sha256":"1919a19cb7a320369301a5a29fc11e861e363371a5170856769bfa1b5ec5ce05","schema_version":"1.0","event_id":"sha256:1919a19cb7a320369301a5a29fc11e861e363371a5170856769bfa1b5ec5ce05"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/bundle.json","state_url":"https://pith.science/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/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":"2026-08-20T06:48:47Z","links":{"resolver":"https://pith.science/pith/YBNQ5GSJE7B5WAODRJNSETTEZG","bundle":"https://pith.science/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/bundle.json","state":"https://pith.science/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YBNQ5GSJE7B5WAODRJNSETTEZG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YBNQ5GSJE7B5WAODRJNSETTEZG","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":"4610eccdcefe680e9ddaccb8a007ef80c97af5825142b237d481f3dbc0be8715","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-02T09:07:19Z","title_canon_sha256":"723265c0eead864cc72c8c0ed1b32d78231269fa237d0408dbbd6267e8dd9781"},"schema_version":"1.0","source":{"id":"2407.02074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.02074","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"arxiv_version","alias_value":"2407.02074v1","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.02074","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_12","alias_value":"YBNQ5GSJE7B5","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_16","alias_value":"YBNQ5GSJE7B5WAOD","created_at":"2026-07-05T08:39:09Z"},{"alias_kind":"pith_short_8","alias_value":"YBNQ5GSJ","created_at":"2026-07-05T08:39:09Z"}],"graph_snapshots":[{"event_id":"sha256:1919a19cb7a320369301a5a29fc11e861e363371a5170856769bfa1b5ec5ce05","target":"graph","created_at":"2026-07-05T08:39:09Z","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/2407.02074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The explosion of massive urban data recently has provided us with a valuable opportunity to gain deeper insights into urban regions and the daily lives of residents. Urban region representation learning emerges as a crucial realm for fulfilling this task. Among deep learning approaches, graph neural networks (GNNs) have shown promise, given that city elements can be naturally represented as nodes with various connections between them as edges. However, many existing GNN approaches encounter challenges such as over-smoothing and limitations in capturing information from nodes in other regions, ","authors_text":"Xiao Zhou, Zhuo Xu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-02T09:07:19Z","title":"CGAP: Urban Region Representation Learning with Coarsened Graph Attention Pooling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.02074","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:dd5a94daf64a90185e93523f47e0022d43cd2e271c741a24c407739004649898","target":"record","created_at":"2026-07-05T08:39:09Z","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":"4610eccdcefe680e9ddaccb8a007ef80c97af5825142b237d481f3dbc0be8715","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-02T09:07:19Z","title_canon_sha256":"723265c0eead864cc72c8c0ed1b32d78231269fa237d0408dbbd6267e8dd9781"},"schema_version":"1.0","source":{"id":"2407.02074","kind":"arxiv","version":1}},"canonical_sha256":"c05b0e9a4927c3db01c38a5b224e64c9b703ac485ad611c179018e5f07d74262","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c05b0e9a4927c3db01c38a5b224e64c9b703ac485ad611c179018e5f07d74262","first_computed_at":"2026-07-05T08:39:09.709802Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:39:09.709802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gC6ZCPw8XsVpVp38mbV9/ti4Yhs2o861ItK43KZew6V7lPWRP9YS33B6x52dBYXj8Y1NpOXCfpql7kmURg6aBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:39:09.710280Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.02074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dd5a94daf64a90185e93523f47e0022d43cd2e271c741a24c407739004649898","sha256:1919a19cb7a320369301a5a29fc11e861e363371a5170856769bfa1b5ec5ce05"],"state_sha256":"fc163e96b584dafaeeb2fd5bf7bfa659748305d8b3b2a75798fd136fa7f3c399"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D8mbZUohidQSG1oCpFfo7KNbgl/8zEUa1a/suhN6bwxa+5J8Dh/uNCtckBY3QHzROiw0xG89luzf52mFEIfWBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T06:48:47.878594Z","bundle_sha256":"8732c101a5cd895cab3fb32ba4f401cd10538c34b9b43a2766d071fae3727895"}}