{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:V7UAQXYUVMKN7RBLRACVHO6AHG","short_pith_number":"pith:V7UAQXYU","canonical_record":{"source":{"id":"1910.01736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T03:17:30Z","cross_cats_sorted":["cs.SI","eess.IV","eess.SP","stat.ML"],"title_canon_sha256":"8d979dd065930845b78686d5f25ceb7cd607eadeb7dd828b3f82faf1778153fa","abstract_canon_sha256":"0f1d23ad43e745949f1ff8036a4ef66bcd1f5d97f5fe8fd10048e1d23b24538d"},"schema_version":"1.0"},"canonical_sha256":"afe8085f14ab14dfc42b880553bbc039b927969904833e1d5ce9aedbd7878ebc","source":{"kind":"arxiv","id":"1910.01736","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01736","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01736v1","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01736","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"V7UAQXYUVMKN","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_16","alias_value":"V7UAQXYUVMKN7RBL","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_8","alias_value":"V7UAQXYU","created_at":"2026-07-05T00:09:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:V7UAQXYUVMKN7RBLRACVHO6AHG","target":"record","payload":{"canonical_record":{"source":{"id":"1910.01736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T03:17:30Z","cross_cats_sorted":["cs.SI","eess.IV","eess.SP","stat.ML"],"title_canon_sha256":"8d979dd065930845b78686d5f25ceb7cd607eadeb7dd828b3f82faf1778153fa","abstract_canon_sha256":"0f1d23ad43e745949f1ff8036a4ef66bcd1f5d97f5fe8fd10048e1d23b24538d"},"schema_version":"1.0"},"canonical_sha256":"afe8085f14ab14dfc42b880553bbc039b927969904833e1d5ce9aedbd7878ebc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:09:53.049711Z","signature_b64":"m0ynz43FyqvyGk03n4rYyyNZigbAitavNoGyQOhf8evUgGBkKPvCLXJdZO6hn20r0CXGvn+dDO9vQbQ5AtiCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afe8085f14ab14dfc42b880553bbc039b927969904833e1d5ce9aedbd7878ebc","last_reissued_at":"2026-07-05T00:09:53.049357Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:09:53.049357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.01736","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":"2026-07-05T00:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YOCxU9s46aeJaity/FYca2Q04dTAKm71bTo97wpbrESZTEmJSeUpQk45j4tLz2ZJ1ea/ngCTcqGUTCCjvokxBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T06:20:41.074130Z"},"content_sha256":"998e9c1db2e9ec7d9b993e5254425b96225f5857b99a797f1a0485e95390b998","schema_version":"1.0","event_id":"sha256:998e9c1db2e9ec7d9b993e5254425b96225f5857b99a797f1a0485e95390b998"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:V7UAQXYUVMKN7RBLRACVHO6AHG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Context-Aware Graph Attention Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","eess.IV","eess.SP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bin Luo, Bo Jiang, Jin Tang, Leiling Wang","submitted_at":"2019-09-04T03:17:30Z","abstract_excerpt":"Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation. In this paper, we propose a novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT). CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges. In particular, CaGAT conducts context-aware learning on both node f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01736","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/1910.01736/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":"2026-07-05T00:09:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cSJqZ9FOIhqqOaFl+Z7Zdg0GGIIDwxoLuXq6Osv1XmjbNa6sC6vXNy26/ZIOsallB9q597XqLlKq4SU4vrHyCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T06:20:41.074727Z"},"content_sha256":"404924554dfd8f6467621596db4417c65909e5740dd75c216bb0e92a302bb85d","schema_version":"1.0","event_id":"sha256:404924554dfd8f6467621596db4417c65909e5740dd75c216bb0e92a302bb85d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/bundle.json","state_url":"https://pith.science/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/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":"2026-08-23T06:20:41Z","links":{"resolver":"https://pith.science/pith/V7UAQXYUVMKN7RBLRACVHO6AHG","bundle":"https://pith.science/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/bundle.json","state":"https://pith.science/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V7UAQXYUVMKN7RBLRACVHO6AHG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:V7UAQXYUVMKN7RBLRACVHO6AHG","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":"0f1d23ad43e745949f1ff8036a4ef66bcd1f5d97f5fe8fd10048e1d23b24538d","cross_cats_sorted":["cs.SI","eess.IV","eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T03:17:30Z","title_canon_sha256":"8d979dd065930845b78686d5f25ceb7cd607eadeb7dd828b3f82faf1778153fa"},"schema_version":"1.0","source":{"id":"1910.01736","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01736","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01736v1","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01736","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_12","alias_value":"V7UAQXYUVMKN","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_16","alias_value":"V7UAQXYUVMKN7RBL","created_at":"2026-07-05T00:09:53Z"},{"alias_kind":"pith_short_8","alias_value":"V7UAQXYU","created_at":"2026-07-05T00:09:53Z"}],"graph_snapshots":[{"event_id":"sha256:404924554dfd8f6467621596db4417c65909e5740dd75c216bb0e92a302bb85d","target":"graph","created_at":"2026-07-05T00:09:53Z","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/1910.01736/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation. In this paper, we propose a novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT). CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges. In particular, CaGAT conducts context-aware learning on both node f","authors_text":"Bin Luo, Bo Jiang, Jin Tang, Leiling Wang","cross_cats":["cs.SI","eess.IV","eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T03:17:30Z","title":"Context-Aware Graph Attention Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01736","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":"sha256:998e9c1db2e9ec7d9b993e5254425b96225f5857b99a797f1a0485e95390b998","target":"record","created_at":"2026-07-05T00:09:53Z","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":"0f1d23ad43e745949f1ff8036a4ef66bcd1f5d97f5fe8fd10048e1d23b24538d","cross_cats_sorted":["cs.SI","eess.IV","eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-09-04T03:17:30Z","title_canon_sha256":"8d979dd065930845b78686d5f25ceb7cd607eadeb7dd828b3f82faf1778153fa"},"schema_version":"1.0","source":{"id":"1910.01736","kind":"arxiv","version":1}},"canonical_sha256":"afe8085f14ab14dfc42b880553bbc039b927969904833e1d5ce9aedbd7878ebc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afe8085f14ab14dfc42b880553bbc039b927969904833e1d5ce9aedbd7878ebc","first_computed_at":"2026-07-05T00:09:53.049357Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:09:53.049357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m0ynz43FyqvyGk03n4rYyyNZigbAitavNoGyQOhf8evUgGBkKPvCLXJdZO6hn20r0CXGvn+dDO9vQbQ5AtiCCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:09:53.049711Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.01736","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:998e9c1db2e9ec7d9b993e5254425b96225f5857b99a797f1a0485e95390b998","sha256:404924554dfd8f6467621596db4417c65909e5740dd75c216bb0e92a302bb85d"],"state_sha256":"ab22b26c7248d71d797398fb30ac56862cabeb9857317b59c3d2381e66e9a80d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tfcgBElrLbU8ALTeACyVbUpiTgnD9DVDyUjGFH/gPXqM6e8JRyH3DaTKW7rvOTjvZO5HshvtYbHxdtQi3ctwCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T06:20:41.079819Z","bundle_sha256":"626b78eb8724de7cfb863a386674eff6b02e5e304f0d0f224ba3838febff825b"}}