{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:WZWEG3DXLFH4K7FVMYLXQUS3G2","short_pith_number":"pith:WZWEG3DX","schema_version":"1.0","canonical_sha256":"b66c436c77594fc57cb5661778525b36a69097fb9b9d7bd3edfbce91ee79d5e9","source":{"kind":"arxiv","id":"2010.13209","version":4},"attestation_state":"computed","paper":{"title":"Multi-Graph Tensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Danilo P. Mandic, Kriton Konstantinidis, Yao Lei Xu","submitted_at":"2020-10-25T20:14:57Z","abstract_excerpt":"The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based methods have demonstrated promising results in bypassing the bottlenecks imposed by the Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which exploits both the ability of graphs to ha"},"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":"2010.13209","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-25T20:14:57Z","cross_cats_sorted":[],"title_canon_sha256":"e0f94f170c68b1dd2f7c9d6a5f86debbda9c19b9e9bab036f73ddd6f1425ab8b","abstract_canon_sha256":"853a44f61977909d60011da520f4bdf1df250d41755c3c921e434aee23fe1ed4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:32.302089Z","signature_b64":"wDhFBz41H5xt58sCyxSxKJ2xaxwEhoczr/yUAN/PmkTHBvCK7TBchtadsdjxKUzQbYYv2l4vV0dEzpWVfZrvDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b66c436c77594fc57cb5661778525b36a69097fb9b9d7bd3edfbce91ee79d5e9","last_reissued_at":"2026-07-05T02:08:32.301614Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:32.301614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Graph Tensor Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Danilo P. Mandic, Kriton Konstantinidis, Yao Lei Xu","submitted_at":"2020-10-25T20:14:57Z","abstract_excerpt":"The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph topology. At the same time, tensor-based methods have demonstrated promising results in bypassing the bottlenecks imposed by the Curse of Dimensionality. In this paper, we introduce a novel Multi-Graph Tensor Network (MGTN) framework, which exploits both the ability of graphs to ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.13209","kind":"arxiv","version":4},"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/2010.13209/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":"2010.13209","created_at":"2026-07-05T02:08:32.301673+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.13209v4","created_at":"2026-07-05T02:08:32.301673+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.13209","created_at":"2026-07-05T02:08:32.301673+00:00"},{"alias_kind":"pith_short_12","alias_value":"WZWEG3DXLFH4","created_at":"2026-07-05T02:08:32.301673+00:00"},{"alias_kind":"pith_short_16","alias_value":"WZWEG3DXLFH4K7FV","created_at":"2026-07-05T02:08:32.301673+00:00"},{"alias_kind":"pith_short_8","alias_value":"WZWEG3DX","created_at":"2026-07-05T02:08:32.301673+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/WZWEG3DXLFH4K7FVMYLXQUS3G2","json":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2.json","graph_json":"https://pith.science/api/pith-number/WZWEG3DXLFH4K7FVMYLXQUS3G2/graph.json","events_json":"https://pith.science/api/pith-number/WZWEG3DXLFH4K7FVMYLXQUS3G2/events.json","paper":"https://pith.science/paper/WZWEG3DX"},"agent_actions":{"view_html":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2","download_json":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2.json","view_paper":"https://pith.science/paper/WZWEG3DX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.13209&json=true","fetch_graph":"https://pith.science/api/pith-number/WZWEG3DXLFH4K7FVMYLXQUS3G2/graph.json","fetch_events":"https://pith.science/api/pith-number/WZWEG3DXLFH4K7FVMYLXQUS3G2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2/action/storage_attestation","attest_author":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2/action/author_attestation","sign_citation":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2/action/citation_signature","submit_replication":"https://pith.science/pith/WZWEG3DXLFH4K7FVMYLXQUS3G2/action/replication_record"}},"created_at":"2026-07-05T02:08:32.301673+00:00","updated_at":"2026-07-05T02:08:32.301673+00:00"}