{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:OPUTCB6J4CMD6QU4HHOMIUXMDC","short_pith_number":"pith:OPUTCB6J","canonical_record":{"source":{"id":"1905.11485","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T20:23:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"da8f4ff357b15e0f078c7407c7d5c9a71e4e70daec51b36e58c4ef5e1893b8f8","abstract_canon_sha256":"8bd4f03661f6aba084e28ded47a7dd9c9117ab0f035ee89f49da1461fce99e38"},"schema_version":"1.0"},"canonical_sha256":"73e93107c9e0983f429c39dcc452ec18ab5bb7bf946a655ab2980548bad737ea","source":{"kind":"arxiv","id":"1905.11485","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11485","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11485v2","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11485","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"OPUTCB6J4CMD","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_16","alias_value":"OPUTCB6J4CMD6QU4","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_8","alias_value":"OPUTCB6J","created_at":"2026-07-05T00:58:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:OPUTCB6J4CMD6QU4HHOMIUXMDC","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11485","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T20:23:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"da8f4ff357b15e0f078c7407c7d5c9a71e4e70daec51b36e58c4ef5e1893b8f8","abstract_canon_sha256":"8bd4f03661f6aba084e28ded47a7dd9c9117ab0f035ee89f49da1461fce99e38"},"schema_version":"1.0"},"canonical_sha256":"73e93107c9e0983f429c39dcc452ec18ab5bb7bf946a655ab2980548bad737ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:58:23.689016Z","signature_b64":"JdIWY5u9liSObcvBbwt4QVtm7QI82lM9knpBuVF98zx0HG32q6zwMMLEI7crX3U1WQ+29BSXjLIBRinVgpAIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73e93107c9e0983f429c39dcc452ec18ab5bb7bf946a655ab2980548bad737ea","last_reissued_at":"2026-07-05T00:58:23.688604Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:58:23.688604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11485","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":"2026-07-05T00:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wrexSqQ6yyZEGX3W0lN1bryrM4n0Mqn7OlAVneW7WDbAcTceiYjoiGIgK1c8A8oP5MFGn3fI8JQV1gkW+mIZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T05:30:23.797938Z"},"content_sha256":"8b9c3beca863986197f96c7a62a3f6304dd228a470e806086b7c05e536098faf","schema_version":"1.0","event_id":"sha256:8b9c3beca863986197f96c7a62a3f6304dd228a470e806086b7c05e536098faf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:OPUTCB6J4CMD6QU4HHOMIUXMDC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Representation Learning for Dynamic Graphs: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Akshay Sethi, Ivan Kobyzev, Kshitij Jain, Pascal Poupart, Peter Forsyth, Rishab Goel, Seyed Mehran Kazemi","submitted_at":"2019-05-27T20:23:02Z","abstract_excerpt":"Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder pers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11485","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/1905.11485/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":"2026-07-05T00:58:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s/5Gd819cSQ1lfydVIsI17OKcAywFLO1alvpiHyt+jlcdnfPPJzpExETckya1f8OLPyPCTFRnNpzKN/P0voxBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T05:30:23.798503Z"},"content_sha256":"74a6962cd7d3b0c7f5da97e01146ee97a168eee688baf473df37bc7a44dc528b","schema_version":"1.0","event_id":"sha256:74a6962cd7d3b0c7f5da97e01146ee97a168eee688baf473df37bc7a44dc528b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/bundle.json","state_url":"https://pith.science/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/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":"2026-08-23T05:30:23Z","links":{"resolver":"https://pith.science/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC","bundle":"https://pith.science/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/bundle.json","state":"https://pith.science/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OPUTCB6J4CMD6QU4HHOMIUXMDC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OPUTCB6J4CMD6QU4HHOMIUXMDC","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":"8bd4f03661f6aba084e28ded47a7dd9c9117ab0f035ee89f49da1461fce99e38","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T20:23:02Z","title_canon_sha256":"da8f4ff357b15e0f078c7407c7d5c9a71e4e70daec51b36e58c4ef5e1893b8f8"},"schema_version":"1.0","source":{"id":"1905.11485","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11485","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11485v2","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11485","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_12","alias_value":"OPUTCB6J4CMD","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_16","alias_value":"OPUTCB6J4CMD6QU4","created_at":"2026-07-05T00:58:23Z"},{"alias_kind":"pith_short_8","alias_value":"OPUTCB6J","created_at":"2026-07-05T00:58:23Z"}],"graph_snapshots":[{"event_id":"sha256:74a6962cd7d3b0c7f5da97e01146ee97a168eee688baf473df37bc7a44dc528b","target":"graph","created_at":"2026-07-05T00:58:23Z","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/1905.11485/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder pers","authors_text":"Akshay Sethi, Ivan Kobyzev, Kshitij Jain, Pascal Poupart, Peter Forsyth, Rishab Goel, Seyed Mehran Kazemi","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T20:23:02Z","title":"Representation Learning for Dynamic Graphs: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11485","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":"sha256:8b9c3beca863986197f96c7a62a3f6304dd228a470e806086b7c05e536098faf","target":"record","created_at":"2026-07-05T00:58:23Z","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":"8bd4f03661f6aba084e28ded47a7dd9c9117ab0f035ee89f49da1461fce99e38","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-05-27T20:23:02Z","title_canon_sha256":"da8f4ff357b15e0f078c7407c7d5c9a71e4e70daec51b36e58c4ef5e1893b8f8"},"schema_version":"1.0","source":{"id":"1905.11485","kind":"arxiv","version":2}},"canonical_sha256":"73e93107c9e0983f429c39dcc452ec18ab5bb7bf946a655ab2980548bad737ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73e93107c9e0983f429c39dcc452ec18ab5bb7bf946a655ab2980548bad737ea","first_computed_at":"2026-07-05T00:58:23.688604Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:58:23.688604Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JdIWY5u9liSObcvBbwt4QVtm7QI82lM9knpBuVF98zx0HG32q6zwMMLEI7crX3U1WQ+29BSXjLIBRinVgpAIDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:58:23.689016Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11485","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b9c3beca863986197f96c7a62a3f6304dd228a470e806086b7c05e536098faf","sha256:74a6962cd7d3b0c7f5da97e01146ee97a168eee688baf473df37bc7a44dc528b"],"state_sha256":"2d45fd89cc3980b78556e9a930793435c806d4ce953672506ca60a0021890009"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9D5VxXYXPnaIoCOpY4DiaKxx1FBgefw/XGZyzZ+3fh335RDlgzG83exaScRMLaG84IsdXB65MgjV8NVCU7IaBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T05:30:23.803339Z","bundle_sha256":"a1e3f5eecc05703f49a4b2b83f0eaeabcc469d23b9fae7d92a2e2056a0e24372"}}