{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LC7P7XQH6TEVTVFR5RLKG4W7C6","short_pith_number":"pith:LC7P7XQH","schema_version":"1.0","canonical_sha256":"58beffde07f4c959d4b1ec56a372df17a2971db645e55e2b6412bad55e9735cd","source":{"kind":"arxiv","id":"2406.17517","version":1},"attestation_state":"computed","paper":{"title":"Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuicui Luo, Ge Chen, Sheng Ouyang, Yong Liu, Yulan Hu","submitted_at":"2024-06-25T12:54:35Z","abstract_excerpt":"Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, such as mean-square-error (MSE), to reconstruct the input graph. However, relying solely on a single reconstruction criterion may lead to a loss of distinctiveness in the reconstructed graph, causing nodes to collapse into similar representations and resulting in sub-optimal performance. To address this issue, we have developed a simple yet effective strategy to preserve the necessary distinctness in the reconstructed "},"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":"2406.17517","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-25T12:54:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"781c7adcfdc50dc7f09cbd31fab07685dbf8e90f20252de925ccbbf4eb5f00d2","abstract_canon_sha256":"80224bcfae9b7becaa80b04bd47e44d489b542967d2aec1fc32b94b6722c77a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:36.184593Z","signature_b64":"wQMjmhE4QLTneYXReFJ9IwPM8eEPgeUTn49on0phYxxUwW9bhyXZMYeSdZgmNgXs4qdRAMG/h3hZdiZnRXtcCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58beffde07f4c959d4b1ec56a372df17a2971db645e55e2b6412bad55e9735cd","last_reissued_at":"2026-07-05T08:36:36.184115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:36.184115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Cuicui Luo, Ge Chen, Sheng Ouyang, Yong Liu, Yulan Hu","submitted_at":"2024-06-25T12:54:35Z","abstract_excerpt":"Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach, have shown great potential in recent years. GAEs typically rely on distance-based criteria, such as mean-square-error (MSE), to reconstruct the input graph. However, relying solely on a single reconstruction criterion may lead to a loss of distinctiveness in the reconstructed graph, causing nodes to collapse into similar representations and resulting in sub-optimal performance. To address this issue, we have developed a simple yet effective strategy to preserve the necessary distinctness in the reconstructed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17517","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/2406.17517/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":"2406.17517","created_at":"2026-07-05T08:36:36.184177+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.17517v1","created_at":"2026-07-05T08:36:36.184177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17517","created_at":"2026-07-05T08:36:36.184177+00:00"},{"alias_kind":"pith_short_12","alias_value":"LC7P7XQH6TEV","created_at":"2026-07-05T08:36:36.184177+00:00"},{"alias_kind":"pith_short_16","alias_value":"LC7P7XQH6TEVTVFR","created_at":"2026-07-05T08:36:36.184177+00:00"},{"alias_kind":"pith_short_8","alias_value":"LC7P7XQH","created_at":"2026-07-05T08:36:36.184177+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/LC7P7XQH6TEVTVFR5RLKG4W7C6","json":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6.json","graph_json":"https://pith.science/api/pith-number/LC7P7XQH6TEVTVFR5RLKG4W7C6/graph.json","events_json":"https://pith.science/api/pith-number/LC7P7XQH6TEVTVFR5RLKG4W7C6/events.json","paper":"https://pith.science/paper/LC7P7XQH"},"agent_actions":{"view_html":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6","download_json":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6.json","view_paper":"https://pith.science/paper/LC7P7XQH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.17517&json=true","fetch_graph":"https://pith.science/api/pith-number/LC7P7XQH6TEVTVFR5RLKG4W7C6/graph.json","fetch_events":"https://pith.science/api/pith-number/LC7P7XQH6TEVTVFR5RLKG4W7C6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6/action/storage_attestation","attest_author":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6/action/author_attestation","sign_citation":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6/action/citation_signature","submit_replication":"https://pith.science/pith/LC7P7XQH6TEVTVFR5RLKG4W7C6/action/replication_record"}},"created_at":"2026-07-05T08:36:36.184177+00:00","updated_at":"2026-07-05T08:36:36.184177+00:00"}