{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5TYXBAH4VHXXEC6P6BFEKXMMTJ","short_pith_number":"pith:5TYXBAH4","schema_version":"1.0","canonical_sha256":"ecf17080fca9ef720bcff04a455d8c9a4e7c3fe50f9bbd3366059560c9f9b8b9","source":{"kind":"arxiv","id":"2402.01614","version":1},"attestation_state":"computed","paper":{"title":"L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Elliott, Gesine Reinert, Mihai Cucuringu, RuiKang OuYang, Stratis Limnios","submitted_at":"2024-02-02T18:24:37Z","abstract_excerpt":"For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are obtained through minimising a loss function; these embeddings are used with a decoder for downstream tasks such as node classification and edge prediction. While GAEs tend to be fairly accurate, they suffer from scalability issues. For improved speed, a Local2Global approach, which combines graph patch embeddings based on eigenvector synchronisation, was shown to be fast and ach"},"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":"2402.01614","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-02-02T18:24:37Z","cross_cats_sorted":["cs.AI","cs.SI","stat.ML"],"title_canon_sha256":"6dbd7f4217ce701c6d3cb1db37b7be089fcd6affce8b7edc799bb1bd0374f74d","abstract_canon_sha256":"de18216dbe3d83f14a97a94065caf992e460c91adee09e93c80aa0bcd661df75"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:40:46.401632Z","signature_b64":"soexzeIXGzcltrfSVEGU1cM54OzYBrLUIz2IH4edX7LmcHs7YoIDWGSwn/62Z34nUxk8ujcVGhDBuNjXypd1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecf17080fca9ef720bcff04a455d8c9a4e7c3fe50f9bbd3366059560c9f9b8b9","last_reissued_at":"2026-07-05T07:40:46.401155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:40:46.401155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Andrew Elliott, Gesine Reinert, Mihai Cucuringu, RuiKang OuYang, Stratis Limnios","submitted_at":"2024-02-02T18:24:37Z","abstract_excerpt":"For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are obtained through minimising a loss function; these embeddings are used with a decoder for downstream tasks such as node classification and edge prediction. While GAEs tend to be fairly accurate, they suffer from scalability issues. For improved speed, a Local2Global approach, which combines graph patch embeddings based on eigenvector synchronisation, was shown to be fast and ach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.01614","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/2402.01614/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":"2402.01614","created_at":"2026-07-05T07:40:46.401211+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.01614v1","created_at":"2026-07-05T07:40:46.401211+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.01614","created_at":"2026-07-05T07:40:46.401211+00:00"},{"alias_kind":"pith_short_12","alias_value":"5TYXBAH4VHXX","created_at":"2026-07-05T07:40:46.401211+00:00"},{"alias_kind":"pith_short_16","alias_value":"5TYXBAH4VHXXEC6P","created_at":"2026-07-05T07:40:46.401211+00:00"},{"alias_kind":"pith_short_8","alias_value":"5TYXBAH4","created_at":"2026-07-05T07:40:46.401211+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/5TYXBAH4VHXXEC6P6BFEKXMMTJ","json":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ.json","graph_json":"https://pith.science/api/pith-number/5TYXBAH4VHXXEC6P6BFEKXMMTJ/graph.json","events_json":"https://pith.science/api/pith-number/5TYXBAH4VHXXEC6P6BFEKXMMTJ/events.json","paper":"https://pith.science/paper/5TYXBAH4"},"agent_actions":{"view_html":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ","download_json":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ.json","view_paper":"https://pith.science/paper/5TYXBAH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.01614&json=true","fetch_graph":"https://pith.science/api/pith-number/5TYXBAH4VHXXEC6P6BFEKXMMTJ/graph.json","fetch_events":"https://pith.science/api/pith-number/5TYXBAH4VHXXEC6P6BFEKXMMTJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ/action/storage_attestation","attest_author":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ/action/author_attestation","sign_citation":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ/action/citation_signature","submit_replication":"https://pith.science/pith/5TYXBAH4VHXXEC6P6BFEKXMMTJ/action/replication_record"}},"created_at":"2026-07-05T07:40:46.401211+00:00","updated_at":"2026-07-05T07:40:46.401211+00:00"}