{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:ZQZRMKD277IK7NCW2POHKTZF2F","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":"08bdf07aaa40753abe7d689534345c6a66eae47f95d9ed1fff0dd2f6b1006a88","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T08:27:25Z","title_canon_sha256":"59a1c8b1264a64c8c63b89947ce1c25d89dc681560dc0c276d114a63e26dd126"},"schema_version":"1.0","source":{"id":"2412.08187","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.08187","created_at":"2026-07-05T09:47:45Z"},{"alias_kind":"arxiv_version","alias_value":"2412.08187v1","created_at":"2026-07-05T09:47:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08187","created_at":"2026-07-05T09:47:45Z"},{"alias_kind":"pith_short_12","alias_value":"ZQZRMKD277IK","created_at":"2026-07-05T09:47:45Z"},{"alias_kind":"pith_short_16","alias_value":"ZQZRMKD277IK7NCW","created_at":"2026-07-05T09:47:45Z"},{"alias_kind":"pith_short_8","alias_value":"ZQZRMKD2","created_at":"2026-07-05T09:47:45Z"}],"graph_snapshots":[{"event_id":"sha256:e302f0be37ac1a1b83cb64d2294b1a746b749c84f099958c40d4789be4bf9efa","target":"graph","created_at":"2026-07-05T09:47:45Z","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/2412.08187/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems can be modelled by networks, and network theory provides a useful set of methods to analyze them. Among these methods, graph embedding is a powerful tool to summarize the interactions and topology of a network in a vectorized feature space. When used in input of machine learning algorithms, embedding vectors help with common graph problems such as link prediction, graph matching, etc. Word embeddi","authors_text":"Nicolas Dugu\\'e, Simon Guillot, Thibault Prouteau","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T08:27:25Z","title":"From communities to interpretable network and word embedding: an unified approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08187","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:a951b1850079f19e15a87f2b39a7ba880cd45ffaa45ca61425f297c21e57d6d0","target":"record","created_at":"2026-07-05T09:47:45Z","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":"08bdf07aaa40753abe7d689534345c6a66eae47f95d9ed1fff0dd2f6b1006a88","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T08:27:25Z","title_canon_sha256":"59a1c8b1264a64c8c63b89947ce1c25d89dc681560dc0c276d114a63e26dd126"},"schema_version":"1.0","source":{"id":"2412.08187","kind":"arxiv","version":1}},"canonical_sha256":"cc3316287affd0afb456d3dc754f25d1714c7e2a43ec7b6fb69045764a8550db","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc3316287affd0afb456d3dc754f25d1714c7e2a43ec7b6fb69045764a8550db","first_computed_at":"2026-07-05T09:47:45.736439Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:47:45.736439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xfy4j6O/7Rzb1JHV/89q+Uphgy7XZs/+D/QKvfUVz/IbG+VK1Sy3ap0QVOB3C4GwZs7EGUlts2aODGbSvddMAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:47:45.736898Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.08187","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a951b1850079f19e15a87f2b39a7ba880cd45ffaa45ca61425f297c21e57d6d0","sha256:e302f0be37ac1a1b83cb64d2294b1a746b749c84f099958c40d4789be4bf9efa"],"state_sha256":"4e550cd690888b8eb72d31003ad96e6298e4dea7dc9427ac28377aa8e5ecadde"}