{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZQZRMKD277IK7NCW2POHKTZF2F","short_pith_number":"pith:ZQZRMKD2","schema_version":"1.0","canonical_sha256":"cc3316287affd0afb456d3dc754f25d1714c7e2a43ec7b6fb69045764a8550db","source":{"kind":"arxiv","id":"2412.08187","version":1},"attestation_state":"computed","paper":{"title":"From communities to interpretable network and word embedding: an unified approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Nicolas Dugu\\'e, Simon Guillot, Thibault Prouteau","submitted_at":"2024-12-11T08:27:25Z","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.08187","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-11T08:27:25Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"59a1c8b1264a64c8c63b89947ce1c25d89dc681560dc0c276d114a63e26dd126","abstract_canon_sha256":"08bdf07aaa40753abe7d689534345c6a66eae47f95d9ed1fff0dd2f6b1006a88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:45.736898Z","signature_b64":"xfy4j6O/7Rzb1JHV/89q+Uphgy7XZs/+D/QKvfUVz/IbG+VK1Sy3ap0QVOB3C4GwZs7EGUlts2aODGbSvddMAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc3316287affd0afb456d3dc754f25d1714c7e2a43ec7b6fb69045764a8550db","last_reissued_at":"2026-07-05T09:47:45.736439Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:45.736439Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From communities to interpretable network and word embedding: an unified approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Nicolas Dugu\\'e, Simon Guillot, Thibault Prouteau","submitted_at":"2024-12-11T08:27:25Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.08187","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/2412.08187/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.08187","created_at":"2026-07-05T09:47:45.736494+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.08187v1","created_at":"2026-07-05T09:47:45.736494+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.08187","created_at":"2026-07-05T09:47:45.736494+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZQZRMKD277IK","created_at":"2026-07-05T09:47:45.736494+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZQZRMKD277IK7NCW","created_at":"2026-07-05T09:47:45.736494+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZQZRMKD2","created_at":"2026-07-05T09:47:45.736494+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F","json":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F.json","graph_json":"https://pith.science/api/pith-number/ZQZRMKD277IK7NCW2POHKTZF2F/graph.json","events_json":"https://pith.science/api/pith-number/ZQZRMKD277IK7NCW2POHKTZF2F/events.json","paper":"https://pith.science/paper/ZQZRMKD2"},"agent_actions":{"view_html":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F","download_json":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F.json","view_paper":"https://pith.science/paper/ZQZRMKD2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.08187&json=true","fetch_graph":"https://pith.science/api/pith-number/ZQZRMKD277IK7NCW2POHKTZF2F/graph.json","fetch_events":"https://pith.science/api/pith-number/ZQZRMKD277IK7NCW2POHKTZF2F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F/action/storage_attestation","attest_author":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F/action/author_attestation","sign_citation":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F/action/citation_signature","submit_replication":"https://pith.science/pith/ZQZRMKD277IK7NCW2POHKTZF2F/action/replication_record"}},"created_at":"2026-07-05T09:47:45.736494+00:00","updated_at":"2026-07-05T09:47:45.736494+00:00"}