{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XXWPQ7EFLRZYZZVRYM3Q5HWZZU","short_pith_number":"pith:XXWPQ7EF","schema_version":"1.0","canonical_sha256":"bdecf87c855c738ce6b1c3370e9ed9cd0f26bfb7f8e0600d353fdb01333ddeb8","source":{"kind":"arxiv","id":"1901.08585","version":1},"attestation_state":"computed","paper":{"title":"Graph heat mixture model learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard","submitted_at":"2019-01-24T18:58:31Z","abstract_excerpt":"Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfu"},"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":"1901.08585","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-24T18:58:31Z","cross_cats_sorted":["cs.SI","stat.ML"],"title_canon_sha256":"aaead9f1c30e88683bdb9ac6b91128d2c637ea00b3973e21316e33e95634fddf","abstract_canon_sha256":"012e31ad0c3f065e173e24e4413eaf225ba3ff6e5ba96aa7b361992f8504507d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:35.106166Z","signature_b64":"sKL4PVJwQy5gbvECgnYH1ekcjhQoR88Wtk/8X6Bqq2qAthn3wqES/ZTrjM3gQegNaeFc0MsSWqI040snHB1NBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdecf87c855c738ce6b1c3370e9ed9cd0f26bfb7f8e0600d353fdb01333ddeb8","last_reissued_at":"2026-05-17T23:55:35.105698Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:35.105698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph heat mixture model learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard","submitted_at":"2019-01-24T18:58:31Z","abstract_excerpt":"Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.08585","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":"1901.08585","created_at":"2026-05-17T23:55:35.105782+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.08585v1","created_at":"2026-05-17T23:55:35.105782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.08585","created_at":"2026-05-17T23:55:35.105782+00:00"},{"alias_kind":"pith_short_12","alias_value":"XXWPQ7EFLRZY","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XXWPQ7EFLRZYZZVR","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XXWPQ7EF","created_at":"2026-05-18T12:33:33.725879+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/XXWPQ7EFLRZYZZVRYM3Q5HWZZU","json":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU.json","graph_json":"https://pith.science/api/pith-number/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/graph.json","events_json":"https://pith.science/api/pith-number/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/events.json","paper":"https://pith.science/paper/XXWPQ7EF"},"agent_actions":{"view_html":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU","download_json":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU.json","view_paper":"https://pith.science/paper/XXWPQ7EF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.08585&json=true","fetch_graph":"https://pith.science/api/pith-number/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/graph.json","fetch_events":"https://pith.science/api/pith-number/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/action/storage_attestation","attest_author":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/action/author_attestation","sign_citation":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/action/citation_signature","submit_replication":"https://pith.science/pith/XXWPQ7EFLRZYZZVRYM3Q5HWZZU/action/replication_record"}},"created_at":"2026-05-17T23:55:35.105782+00:00","updated_at":"2026-05-17T23:55:35.105782+00:00"}