{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NDZMDY2G75LM42QYAUO2QRXTH5","short_pith_number":"pith:NDZMDY2G","schema_version":"1.0","canonical_sha256":"68f2c1e346ff56ce6a18051da846f33f6aeb8f671a5050af865ad36740d65bcd","source":{"kind":"arxiv","id":"1902.11004","version":1},"attestation_state":"computed","paper":{"title":"Global Vectors for Node Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Adrien Guille, Julien Velcin, Robin Brochier","submitted_at":"2019-02-28T10:46:54Z","abstract_excerpt":"Most network embedding algorithms consist in measuring co-occurrences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has proven to be a relevant choice, there are alternatives, such as GloVe, which has not been investigated yet for network embedding. Even though SGNS better handles non co-occurrence than GloVe, it has a worse time-complexity. In this paper, we propose a matrix factorization approach for network embedding, inspired by GloVe, that better handles non co-occurrence with a competitive time-complexity. We also show how to exte"},"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":"1902.11004","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-28T10:46:54Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"fcfd368562f4a31f0510e8f705c3450a4979c541fce7c8b564e4b70ed37f9880","abstract_canon_sha256":"1d777db6367299c54c4fbf313e2636e83b131698882ac0cb61d151480328350c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:26.102686Z","signature_b64":"TWoW7fqcPgpCT8/jxaPzBAsEzQx0Er8mVAm/FQ5EUrRDWKOFeP03O2P4aU5aGuvV9rYSglDy565gcUjwubucBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"68f2c1e346ff56ce6a18051da846f33f6aeb8f671a5050af865ad36740d65bcd","last_reissued_at":"2026-05-17T23:52:26.101957Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:26.101957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global Vectors for Node Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.CL","authors_text":"Adrien Guille, Julien Velcin, Robin Brochier","submitted_at":"2019-02-28T10:46:54Z","abstract_excerpt":"Most network embedding algorithms consist in measuring co-occurrences of nodes via random walks then learning the embeddings using Skip-Gram with Negative Sampling. While it has proven to be a relevant choice, there are alternatives, such as GloVe, which has not been investigated yet for network embedding. Even though SGNS better handles non co-occurrence than GloVe, it has a worse time-complexity. In this paper, we propose a matrix factorization approach for network embedding, inspired by GloVe, that better handles non co-occurrence with a competitive time-complexity. We also show how to exte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.11004","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":""},"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":"1902.11004","created_at":"2026-05-17T23:52:26.102077+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.11004v1","created_at":"2026-05-17T23:52:26.102077+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.11004","created_at":"2026-05-17T23:52:26.102077+00:00"},{"alias_kind":"pith_short_12","alias_value":"NDZMDY2G75LM","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NDZMDY2G75LM42QY","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NDZMDY2G","created_at":"2026-05-18T12:33:24.271573+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/NDZMDY2G75LM42QYAUO2QRXTH5","json":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5.json","graph_json":"https://pith.science/api/pith-number/NDZMDY2G75LM42QYAUO2QRXTH5/graph.json","events_json":"https://pith.science/api/pith-number/NDZMDY2G75LM42QYAUO2QRXTH5/events.json","paper":"https://pith.science/paper/NDZMDY2G"},"agent_actions":{"view_html":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5","download_json":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5.json","view_paper":"https://pith.science/paper/NDZMDY2G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.11004&json=true","fetch_graph":"https://pith.science/api/pith-number/NDZMDY2G75LM42QYAUO2QRXTH5/graph.json","fetch_events":"https://pith.science/api/pith-number/NDZMDY2G75LM42QYAUO2QRXTH5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5/action/storage_attestation","attest_author":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5/action/author_attestation","sign_citation":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5/action/citation_signature","submit_replication":"https://pith.science/pith/NDZMDY2G75LM42QYAUO2QRXTH5/action/replication_record"}},"created_at":"2026-05-17T23:52:26.102077+00:00","updated_at":"2026-05-17T23:52:26.102077+00:00"}