{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QSZCEUSBWVKXCFJYN6YF5SEZ74","short_pith_number":"pith:QSZCEUSB","schema_version":"1.0","canonical_sha256":"84b2225241b5557115386fb05ec899ff1ef5ff0cfdbd75bcb11b421bbaa3878b","source":{"kind":"arxiv","id":"1805.09980","version":2},"attestation_state":"computed","paper":{"title":"Deep Graph Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Liang Zhao, Lingfei Wu, Xiaojie Guo","submitted_at":"2018-05-25T04:56:07Z","abstract_excerpt":"Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the graphs being generated. Differently, in this paper, we are interested in a new problem named \\emph{Deep Graph Translation}: given an input graph, we want to infer a target graph based on their underlying (both global and local) translation mapping. Graph translation could be high"},"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":"1805.09980","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-25T04:56:07Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"2aead0eaa5b1c046985817cf49ba8f99eee26e3d50d045402f6ee25f77353417","abstract_canon_sha256":"0777c4ef7b7780f40d646dc75c3062dba2832e521c381060b8ab0ca4ee94132a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:37.822960Z","signature_b64":"ba+fPX67M03AFVAlzMdmGjnw8xocB4zDj7IO0HBF819KKYQyua1HcEHAZvEzrxpwgun0h0Vi0DAb+TMdiMRFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84b2225241b5557115386fb05ec899ff1ef5ff0cfdbd75bcb11b421bbaa3878b","last_reissued_at":"2026-05-18T00:12:37.821464Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:37.821464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Graph Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Liang Zhao, Lingfei Wu, Xiaojie Guo","submitted_at":"2018-05-25T04:56:07Z","abstract_excerpt":"Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the graphs being generated. Differently, in this paper, we are interested in a new problem named \\emph{Deep Graph Translation}: given an input graph, we want to infer a target graph based on their underlying (both global and local) translation mapping. Graph translation could be high"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.09980","kind":"arxiv","version":2},"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":"1805.09980","created_at":"2026-05-18T00:12:37.821587+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.09980v2","created_at":"2026-05-18T00:12:37.821587+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.09980","created_at":"2026-05-18T00:12:37.821587+00:00"},{"alias_kind":"pith_short_12","alias_value":"QSZCEUSBWVKX","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QSZCEUSBWVKXCFJY","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QSZCEUSB","created_at":"2026-05-18T12:32:46.962924+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/QSZCEUSBWVKXCFJYN6YF5SEZ74","json":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74.json","graph_json":"https://pith.science/api/pith-number/QSZCEUSBWVKXCFJYN6YF5SEZ74/graph.json","events_json":"https://pith.science/api/pith-number/QSZCEUSBWVKXCFJYN6YF5SEZ74/events.json","paper":"https://pith.science/paper/QSZCEUSB"},"agent_actions":{"view_html":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74","download_json":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74.json","view_paper":"https://pith.science/paper/QSZCEUSB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.09980&json=true","fetch_graph":"https://pith.science/api/pith-number/QSZCEUSBWVKXCFJYN6YF5SEZ74/graph.json","fetch_events":"https://pith.science/api/pith-number/QSZCEUSBWVKXCFJYN6YF5SEZ74/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74/action/storage_attestation","attest_author":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74/action/author_attestation","sign_citation":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74/action/citation_signature","submit_replication":"https://pith.science/pith/QSZCEUSBWVKXCFJYN6YF5SEZ74/action/replication_record"}},"created_at":"2026-05-18T00:12:37.821587+00:00","updated_at":"2026-05-18T00:12:37.821587+00:00"}