{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HLGWFOM53P6TX5O3EDRJDK7L6L","short_pith_number":"pith:HLGWFOM5","canonical_record":{"source":{"id":"1904.12787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T15:59:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"56b39da3d86e611d5dc182eb8e4f3b69ab9ff273a47d1205ec3f132998177a0a","abstract_canon_sha256":"3179e4e3caf0dda4814f508b1a6c7f0cdb41a77e97fd5429d0f7bf27c268282a"},"schema_version":"1.0"},"canonical_sha256":"3acd62b99ddbfd3bf5db20e291abebf2ed7dcdefcfc13fa4ee1333a7246b9e84","source":{"kind":"arxiv","id":"1904.12787","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12787","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12787v2","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12787","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"HLGWFOM53P6T","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HLGWFOM53P6TX5O3","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HLGWFOM5","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HLGWFOM53P6TX5O3EDRJDK7L6L","target":"record","payload":{"canonical_record":{"source":{"id":"1904.12787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T15:59:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"56b39da3d86e611d5dc182eb8e4f3b69ab9ff273a47d1205ec3f132998177a0a","abstract_canon_sha256":"3179e4e3caf0dda4814f508b1a6c7f0cdb41a77e97fd5429d0f7bf27c268282a"},"schema_version":"1.0"},"canonical_sha256":"3acd62b99ddbfd3bf5db20e291abebf2ed7dcdefcfc13fa4ee1333a7246b9e84","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:25.649031Z","signature_b64":"NYYCHRqkFrtnQm7v8qddDZX9p5hxS/aZiXGCOZsbcdJDlIOin6W8x/1BdFTwKczJciNBKigBwdT/UCi4ONN/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3acd62b99ddbfd3bf5db20e291abebf2ed7dcdefcfc13fa4ee1333a7246b9e84","last_reissued_at":"2026-05-17T23:46:25.648549Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:25.648549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.12787","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":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S515V7cvp828JbNcH8+b8AnUw/R4YiQqqcbBap72E/hwrBYQCAWGpcJePyOPAeitVXuM/C5sZPZhyTsgqradCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:22:01.759575Z"},"content_sha256":"c1025aaa4d7ec9e987d70d8817de0046e851f7e763f587783b16302bbb46c6ca","schema_version":"1.0","event_id":"sha256:c1025aaa4d7ec9e987d70d8817de0046e851f7e763f587783b16302bbb46c6ca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HLGWFOM53P6TX5O3EDRJDK7L6L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Matching Networks for Learning the Similarity of Graph Structured Objects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chenjie Gu, Oriol Vinyals, Pushmeet Kohli, Thomas Dullien, Yujia Li","submitted_at":"2019-04-29T15:59:04Z","abstract_excerpt":"This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-gra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12787","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":""},"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":"2026-05-17T23:46:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LjzCIFwF7+eex+Nwyc4LA8iyYmicilUSlI7tW/pxYIEimEirHhNdLo8BcYSSk1pMB9hE/MTUEJ07elaDrjIeBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:22:01.759926Z"},"content_sha256":"5f792385cd87720800e3d86e7976a363b913ec3b4fedaf8dc81b1463420a431a","schema_version":"1.0","event_id":"sha256:5f792385cd87720800e3d86e7976a363b913ec3b4fedaf8dc81b1463420a431a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/bundle.json","state_url":"https://pith.science/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/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":"2026-06-03T17:22:01Z","links":{"resolver":"https://pith.science/pith/HLGWFOM53P6TX5O3EDRJDK7L6L","bundle":"https://pith.science/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/bundle.json","state":"https://pith.science/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HLGWFOM53P6TX5O3EDRJDK7L6L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HLGWFOM53P6TX5O3EDRJDK7L6L","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":"3179e4e3caf0dda4814f508b1a6c7f0cdb41a77e97fd5429d0f7bf27c268282a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T15:59:04Z","title_canon_sha256":"56b39da3d86e611d5dc182eb8e4f3b69ab9ff273a47d1205ec3f132998177a0a"},"schema_version":"1.0","source":{"id":"1904.12787","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.12787","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"arxiv_version","alias_value":"1904.12787v2","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.12787","created_at":"2026-05-17T23:46:25Z"},{"alias_kind":"pith_short_12","alias_value":"HLGWFOM53P6T","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"HLGWFOM53P6TX5O3","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"HLGWFOM5","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:5f792385cd87720800e3d86e7976a363b913ec3b4fedaf8dc81b1463420a431a","target":"graph","created_at":"2026-05-17T23:46:25Z","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"},"paper":{"abstract_excerpt":"This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to produce embedding of graphs in vector spaces that enables efficient similarity reasoning. Second, we propose a novel Graph Matching Network model that, given a pair of graphs as input, computes a similarity score between them by jointly reasoning on the pair through a new cross-gra","authors_text":"Chenjie Gu, Oriol Vinyals, Pushmeet Kohli, Thomas Dullien, Yujia Li","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T15:59:04Z","title":"Graph Matching Networks for Learning the Similarity of Graph Structured Objects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.12787","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:c1025aaa4d7ec9e987d70d8817de0046e851f7e763f587783b16302bbb46c6ca","target":"record","created_at":"2026-05-17T23:46:25Z","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":"3179e4e3caf0dda4814f508b1a6c7f0cdb41a77e97fd5429d0f7bf27c268282a","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-29T15:59:04Z","title_canon_sha256":"56b39da3d86e611d5dc182eb8e4f3b69ab9ff273a47d1205ec3f132998177a0a"},"schema_version":"1.0","source":{"id":"1904.12787","kind":"arxiv","version":2}},"canonical_sha256":"3acd62b99ddbfd3bf5db20e291abebf2ed7dcdefcfc13fa4ee1333a7246b9e84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3acd62b99ddbfd3bf5db20e291abebf2ed7dcdefcfc13fa4ee1333a7246b9e84","first_computed_at":"2026-05-17T23:46:25.648549Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:25.648549Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NYYCHRqkFrtnQm7v8qddDZX9p5hxS/aZiXGCOZsbcdJDlIOin6W8x/1BdFTwKczJciNBKigBwdT/UCi4ONN/CA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:25.649031Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.12787","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1025aaa4d7ec9e987d70d8817de0046e851f7e763f587783b16302bbb46c6ca","sha256:5f792385cd87720800e3d86e7976a363b913ec3b4fedaf8dc81b1463420a431a"],"state_sha256":"4661e5cb17e8b894e38a951fd997ea644bc0f5305f6735366a0243676c92cae4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BZQT9Vp6UGyqVKmNqVvo1l0IH7XTHbsmMBMMGpez1y/r5N3fySufYJJkSGRUQWsefArBp7sK1zYOnePpX2zuAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T17:22:01.761970Z","bundle_sha256":"e9b449dcca3ec761a65905d00eed9d2ea111e8872fc9d0c10dfafd46d64ad52c"}}