{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:XZFQBFLGDEFIYB4CYS34EDRCZW","short_pith_number":"pith:XZFQBFLG","canonical_record":{"source":{"id":"2109.13441","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-28T02:36:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dd61c72034da490b5ac1809a1e3c3c44a1e497a8341827fbf46ee3b05b45b45b","abstract_canon_sha256":"f3f48569e3bb9671fbad3ba25070f0e04b3fbd1f92e51a380cc0585f689ff76e"},"schema_version":"1.0"},"canonical_sha256":"be4b009566190a8c0782c4b7c20e22cd85490909e95b021493002b50f63e6436","source":{"kind":"arxiv","id":"2109.13441","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13441","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13441v2","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13441","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"XZFQBFLGDEFI","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_16","alias_value":"XZFQBFLGDEFIYB4C","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_8","alias_value":"XZFQBFLG","created_at":"2026-07-05T04:18:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:XZFQBFLGDEFIYB4CYS34EDRCZW","target":"record","payload":{"canonical_record":{"source":{"id":"2109.13441","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-28T02:36:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"dd61c72034da490b5ac1809a1e3c3c44a1e497a8341827fbf46ee3b05b45b45b","abstract_canon_sha256":"f3f48569e3bb9671fbad3ba25070f0e04b3fbd1f92e51a380cc0585f689ff76e"},"schema_version":"1.0"},"canonical_sha256":"be4b009566190a8c0782c4b7c20e22cd85490909e95b021493002b50f63e6436","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:18:28.300083Z","signature_b64":"b0e+6SrkmuMwEgsLrmBWuJf41udAuWOyYCYa0ZTV2SMSI0BrLySLwQSPPelp6SrG/6Jpl0V2+QLpKgTQoObODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be4b009566190a8c0782c4b7c20e22cd85490909e95b021493002b50f63e6436","last_reissued_at":"2026-07-05T04:18:28.299671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:18:28.299671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2109.13441","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":"2026-07-05T04:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QgV4FcfkDCu0ndshDox7GRxBM38IoaU1bT7CyHGQs6El7/AkI2VKQ9eIAMpjxIhCxWMM6oSc3vlYtAQTyxLfCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T01:12:32.021977Z"},"content_sha256":"3f7251880ca784e54e030a2fc5fd1315b344a38bddc18a544491bd1a335c73b4","schema_version":"1.0","event_id":"sha256:3f7251880ca784e54e030a2fc5fd1315b344a38bddc18a544491bd1a335c73b4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:XZFQBFLGDEFIYB4CYS34EDRCZW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Apoorva Vikram Singh, George Em Karniadakis, Mengjia Xu","submitted_at":"2021-09-28T02:36:56Z","abstract_excerpt":"Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy. However, recent advances mostly focus on learning node embeddings as deterministic \"vectors\" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space. In this work, we propose an efficient stochastic dynamic graph embedding method (DynG2G) that applies an inductive feed-forward encoder trained with node triplet-based contrasti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.13441","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/2109.13441/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":"2026-07-05T04:18:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9NHU4XZtdEZ3HADAcaaYgt3zCHikfHSmoB/qbha8CYFOBKwpMc9LgIFhVS4ijaTDpkwyIBFeeO9J75b4boHlAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T01:12:32.022582Z"},"content_sha256":"1e1e9890447e1cf2ae1cfc57517abf9f4f6e3fd93e5b565d1f59467f5bde1664","schema_version":"1.0","event_id":"sha256:1e1e9890447e1cf2ae1cfc57517abf9f4f6e3fd93e5b565d1f59467f5bde1664"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/bundle.json","state_url":"https://pith.science/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/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":"2026-08-14T01:12:32Z","links":{"resolver":"https://pith.science/pith/XZFQBFLGDEFIYB4CYS34EDRCZW","bundle":"https://pith.science/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/bundle.json","state":"https://pith.science/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XZFQBFLGDEFIYB4CYS34EDRCZW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XZFQBFLGDEFIYB4CYS34EDRCZW","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":"f3f48569e3bb9671fbad3ba25070f0e04b3fbd1f92e51a380cc0585f689ff76e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-28T02:36:56Z","title_canon_sha256":"dd61c72034da490b5ac1809a1e3c3c44a1e497a8341827fbf46ee3b05b45b45b"},"schema_version":"1.0","source":{"id":"2109.13441","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.13441","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"arxiv_version","alias_value":"2109.13441v2","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.13441","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_12","alias_value":"XZFQBFLGDEFI","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_16","alias_value":"XZFQBFLGDEFIYB4C","created_at":"2026-07-05T04:18:28Z"},{"alias_kind":"pith_short_8","alias_value":"XZFQBFLG","created_at":"2026-07-05T04:18:28Z"}],"graph_snapshots":[{"event_id":"sha256:1e1e9890447e1cf2ae1cfc57517abf9f4f6e3fd93e5b565d1f59467f5bde1664","target":"graph","created_at":"2026-07-05T04:18:28Z","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/2109.13441/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy. However, recent advances mostly focus on learning node embeddings as deterministic \"vectors\" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space. In this work, we propose an efficient stochastic dynamic graph embedding method (DynG2G) that applies an inductive feed-forward encoder trained with node triplet-based contrasti","authors_text":"Apoorva Vikram Singh, George Em Karniadakis, Mengjia Xu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-28T02:36:56Z","title":"DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.13441","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":"sha256:3f7251880ca784e54e030a2fc5fd1315b344a38bddc18a544491bd1a335c73b4","target":"record","created_at":"2026-07-05T04:18:28Z","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":"f3f48569e3bb9671fbad3ba25070f0e04b3fbd1f92e51a380cc0585f689ff76e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-09-28T02:36:56Z","title_canon_sha256":"dd61c72034da490b5ac1809a1e3c3c44a1e497a8341827fbf46ee3b05b45b45b"},"schema_version":"1.0","source":{"id":"2109.13441","kind":"arxiv","version":2}},"canonical_sha256":"be4b009566190a8c0782c4b7c20e22cd85490909e95b021493002b50f63e6436","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be4b009566190a8c0782c4b7c20e22cd85490909e95b021493002b50f63e6436","first_computed_at":"2026-07-05T04:18:28.299671Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:18:28.299671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b0e+6SrkmuMwEgsLrmBWuJf41udAuWOyYCYa0ZTV2SMSI0BrLySLwQSPPelp6SrG/6Jpl0V2+QLpKgTQoObODw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:18:28.300083Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.13441","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f7251880ca784e54e030a2fc5fd1315b344a38bddc18a544491bd1a335c73b4","sha256:1e1e9890447e1cf2ae1cfc57517abf9f4f6e3fd93e5b565d1f59467f5bde1664"],"state_sha256":"b0a1796d61665326f14cc6001bd456672c60cf27f019f3fa71c21c0fd031657b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PC9ReoJ2xREnt3zw3ukmZYcBFanVSQU69RdKoVbtIUaFBMZHedRaB7wjdO3Yb/YALGPnd39OKMqo+o72UCGlDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T01:12:32.027482Z","bundle_sha256":"0675985244e730db7bcf64628d585ce987d119987d9bf346b795fec064eca858"}}