{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:YWSHF4UDOPOUKYHAI5OFXJNK7E","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d5e94d964598ac2e0f50ffa4bc4eff09a16a4466a2f6ee9154878caa1a673187","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-17T19:07:27Z","title_canon_sha256":"9816f7f7e7d44e058f499c9117971ae3a9dbc543c193f1a67cf4ad1169ca9a0d"},"schema_version":"1.0","source":{"id":"2109.08722","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2109.08722","created_at":"2026-07-05T03:27:35Z"},{"alias_kind":"arxiv_version","alias_value":"2109.08722v5","created_at":"2026-07-05T03:27:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.08722","created_at":"2026-07-05T03:27:35Z"},{"alias_kind":"pith_short_12","alias_value":"YWSHF4UDOPOU","created_at":"2026-07-05T03:27:35Z"},{"alias_kind":"pith_short_16","alias_value":"YWSHF4UDOPOUKYHA","created_at":"2026-07-05T03:27:35Z"},{"alias_kind":"pith_short_8","alias_value":"YWSHF4UD","created_at":"2026-07-05T03:27:35Z"}],"graph_snapshots":[{"event_id":"sha256:fa61df2874b609a2032e33044699a4bf23c819b9628951eebbaea06bbc0cbbd3","target":"graph","created_at":"2026-07-05T03:27:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2109.08722/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Dragomir Radev, Irene Li, Vanessa Yan","cross_cats":["cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-17T19:07:27Z","title":"Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.08722","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:04d1ad7282cc69dc1ac2d6a0f57550f91e6bcebd231838d2f7b171cace2f7df2","target":"record","created_at":"2026-07-05T03:27:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d5e94d964598ac2e0f50ffa4bc4eff09a16a4466a2f6ee9154878caa1a673187","cross_cats_sorted":["cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-09-17T19:07:27Z","title_canon_sha256":"9816f7f7e7d44e058f499c9117971ae3a9dbc543c193f1a67cf4ad1169ca9a0d"},"schema_version":"1.0","source":{"id":"2109.08722","kind":"arxiv","version":5}},"canonical_sha256":"c5a472f28373dd4560e0475c5ba5aaf9087ba317aa0a8c739602cd163804c58c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5a472f28373dd4560e0475c5ba5aaf9087ba317aa0a8c739602cd163804c58c","first_computed_at":"2026-07-05T03:27:35.788295Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:27:35.788295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CDD4fo5vXveqC/wqHXUuO7awZ3AQNkkH9mFyRS2uDvaJlaMuIG49vRChzDMBl7kKHAgVvKBV6zheSZPvDxPKBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:27:35.788774Z","signed_message":"canonical_sha256_bytes"},"source_id":"2109.08722","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:04d1ad7282cc69dc1ac2d6a0f57550f91e6bcebd231838d2f7b171cace2f7df2","sha256:fa61df2874b609a2032e33044699a4bf23c819b9628951eebbaea06bbc0cbbd3"],"state_sha256":"3870009bbcda2692f931b3da2fa73151b866a766ba933fb3af93594f85cdde90"}