{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5ZUFB45KTOD3QC4ABXGIINOK3K","short_pith_number":"pith:5ZUFB45K","schema_version":"1.0","canonical_sha256":"ee6850f3aa9b87b80b800dcc8435cada81c580a45dd121e84a9586f0ba35a32f","source":{"kind":"arxiv","id":"1806.07955","version":1},"attestation_state":"computed","paper":{"title":"Growing Better Graphs With Latent-Variable Probabilistic Graph Grammars","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SI","authors_text":"David Chiang, Salvador Aguinaga, Tim Weninger, Xinyi Wang","submitted_at":"2018-06-11T11:36:43Z","abstract_excerpt":"Recent work in graph models has found that probabilistic hyperedge replacement grammars (HRGs) can be extracted from graphs and used to generate new random graphs with graph properties and substructures close to the original. In this paper, we show how to add latent variables to the model, trained using Expectation-Maximization, to generate still better graphs, that is, ones that generalize better to the test data. We evaluate the new method by separating training and test graphs, building the model on the former and measuring the likelihood of the latter, as a more stringent test of how well "},"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":"1806.07955","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-06-11T11:36:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"83f61c082d5d3b5a4a0b1605780068f072b4e88909e183606dc62140b7228daf","abstract_canon_sha256":"05df699f9a0a65ae5f79d91cf1a78c1ab4473cb58e7fd1cc965a09b7602bd375"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:42.497898Z","signature_b64":"V8L50K9AXwC5fKFQEYGURSHZxPc3IgXLO7t13f4EqlVN0jN51PD8qIf7MYHcgpQvNjnIhEFOXUXfDKM98anPBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee6850f3aa9b87b80b800dcc8435cada81c580a45dd121e84a9586f0ba35a32f","last_reissued_at":"2026-05-18T00:12:42.497333Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:42.497333Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Growing Better Graphs With Latent-Variable Probabilistic Graph Grammars","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.SI","authors_text":"David Chiang, Salvador Aguinaga, Tim Weninger, Xinyi Wang","submitted_at":"2018-06-11T11:36:43Z","abstract_excerpt":"Recent work in graph models has found that probabilistic hyperedge replacement grammars (HRGs) can be extracted from graphs and used to generate new random graphs with graph properties and substructures close to the original. In this paper, we show how to add latent variables to the model, trained using Expectation-Maximization, to generate still better graphs, that is, ones that generalize better to the test data. We evaluate the new method by separating training and test graphs, building the model on the former and measuring the likelihood of the latter, as a more stringent test of how well "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07955","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":"1806.07955","created_at":"2026-05-18T00:12:42.497442+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.07955v1","created_at":"2026-05-18T00:12:42.497442+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07955","created_at":"2026-05-18T00:12:42.497442+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ZUFB45KTOD3","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ZUFB45KTOD3QC4A","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ZUFB45K","created_at":"2026-05-18T12:32:08.215937+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/5ZUFB45KTOD3QC4ABXGIINOK3K","json":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K.json","graph_json":"https://pith.science/api/pith-number/5ZUFB45KTOD3QC4ABXGIINOK3K/graph.json","events_json":"https://pith.science/api/pith-number/5ZUFB45KTOD3QC4ABXGIINOK3K/events.json","paper":"https://pith.science/paper/5ZUFB45K"},"agent_actions":{"view_html":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K","download_json":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K.json","view_paper":"https://pith.science/paper/5ZUFB45K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.07955&json=true","fetch_graph":"https://pith.science/api/pith-number/5ZUFB45KTOD3QC4ABXGIINOK3K/graph.json","fetch_events":"https://pith.science/api/pith-number/5ZUFB45KTOD3QC4ABXGIINOK3K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K/action/storage_attestation","attest_author":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K/action/author_attestation","sign_citation":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K/action/citation_signature","submit_replication":"https://pith.science/pith/5ZUFB45KTOD3QC4ABXGIINOK3K/action/replication_record"}},"created_at":"2026-05-18T00:12:42.497442+00:00","updated_at":"2026-05-18T00:12:42.497442+00:00"}