{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YWSHF4UDOPOUKYHAI5OFXJNK7E","short_pith_number":"pith:YWSHF4UD","schema_version":"1.0","canonical_sha256":"c5a472f28373dd4560e0475c5ba5aaf9087ba317aa0a8c739602cd163804c58c","source":{"kind":"arxiv","id":"2109.08722","version":5},"attestation_state":"computed","paper":{"title":"Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Dragomir Radev, Irene Li, Vanessa Yan","submitted_at":"2021-09-17T19:07:27Z","abstract_excerpt":"Prerequisite chain learning helps people acquire new knowledge efficiently. While people may quickly determine learning paths over concepts in a domain, finding such paths in other domains can be challenging. We introduce Domain-Adversarial Variational Graph Autoencoders (DAVGAE) to solve this cross-domain prerequisite chain learning task efficiently. Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator. The VGAE is trained to predict concept relations through link prediction, while the domain discriminator takes both source and target domain data as in"},"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":"2109.08722","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-17T19:07:27Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"9816f7f7e7d44e058f499c9117971ae3a9dbc543c193f1a67cf4ad1169ca9a0d","abstract_canon_sha256":"d5e94d964598ac2e0f50ffa4bc4eff09a16a4466a2f6ee9154878caa1a673187"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:27:35.788774Z","signature_b64":"CDD4fo5vXveqC/wqHXUuO7awZ3AQNkkH9mFyRS2uDvaJlaMuIG49vRChzDMBl7kKHAgVvKBV6zheSZPvDxPKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5a472f28373dd4560e0475c5ba5aaf9087ba317aa0a8c739602cd163804c58c","last_reissued_at":"2026-07-05T03:27:35.788295Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:27:35.788295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Dragomir Radev, Irene Li, Vanessa Yan","submitted_at":"2021-09-17T19:07:27Z","abstract_excerpt":"Prerequisite chain learning helps people acquire new knowledge efficiently. While people may quickly determine learning paths over concepts in a domain, finding such paths in other domains can be challenging. We introduce Domain-Adversarial Variational Graph Autoencoders (DAVGAE) to solve this cross-domain prerequisite chain learning task efficiently. Our novel model consists of a variational graph autoencoder (VGAE) and a domain discriminator. The VGAE is trained to predict concept relations through link prediction, while the domain discriminator takes both source and target domain data as in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08722","kind":"arxiv","version":5},"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/2109.08722/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":"2109.08722","created_at":"2026-07-05T03:27:35.788360+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.08722v5","created_at":"2026-07-05T03:27:35.788360+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08722","created_at":"2026-07-05T03:27:35.788360+00:00"},{"alias_kind":"pith_short_12","alias_value":"YWSHF4UDOPOU","created_at":"2026-07-05T03:27:35.788360+00:00"},{"alias_kind":"pith_short_16","alias_value":"YWSHF4UDOPOUKYHA","created_at":"2026-07-05T03:27:35.788360+00:00"},{"alias_kind":"pith_short_8","alias_value":"YWSHF4UD","created_at":"2026-07-05T03:27:35.788360+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/YWSHF4UDOPOUKYHAI5OFXJNK7E","json":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E.json","graph_json":"https://pith.science/api/pith-number/YWSHF4UDOPOUKYHAI5OFXJNK7E/graph.json","events_json":"https://pith.science/api/pith-number/YWSHF4UDOPOUKYHAI5OFXJNK7E/events.json","paper":"https://pith.science/paper/YWSHF4UD"},"agent_actions":{"view_html":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E","download_json":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E.json","view_paper":"https://pith.science/paper/YWSHF4UD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.08722&json=true","fetch_graph":"https://pith.science/api/pith-number/YWSHF4UDOPOUKYHAI5OFXJNK7E/graph.json","fetch_events":"https://pith.science/api/pith-number/YWSHF4UDOPOUKYHAI5OFXJNK7E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E/action/storage_attestation","attest_author":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E/action/author_attestation","sign_citation":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E/action/citation_signature","submit_replication":"https://pith.science/pith/YWSHF4UDOPOUKYHAI5OFXJNK7E/action/replication_record"}},"created_at":"2026-07-05T03:27:35.788360+00:00","updated_at":"2026-07-05T03:27:35.788360+00:00"}