{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7YBJBALHDOGRKGXTVQ4JUH3ZQJ","short_pith_number":"pith:7YBJBALH","canonical_record":{"source":{"id":"2202.00264","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T07:52:01Z","cross_cats_sorted":["cs.NA","math.NA","math.OC","stat.ML"],"title_canon_sha256":"5828c338e32dd292a8a6f49f3bac4af54e508141695b8dfdc3ee215a60b5b31b","abstract_canon_sha256":"c10089c152c83b7456f1baec5367eca4192fcde47175b3677460be6615c9db0b"},"schema_version":"1.0"},"canonical_sha256":"fe029081671b8d151af3ac389a1f79825aa9929f22667a70b481d399a80ea73d","source":{"kind":"arxiv","id":"2202.00264","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00264","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00264v1","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00264","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_12","alias_value":"7YBJBALHDOGR","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_16","alias_value":"7YBJBALHDOGRKGXT","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_8","alias_value":"7YBJBALH","created_at":"2026-07-05T03:53:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7YBJBALHDOGRKGXTVQ4JUH3ZQJ","target":"record","payload":{"canonical_record":{"source":{"id":"2202.00264","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T07:52:01Z","cross_cats_sorted":["cs.NA","math.NA","math.OC","stat.ML"],"title_canon_sha256":"5828c338e32dd292a8a6f49f3bac4af54e508141695b8dfdc3ee215a60b5b31b","abstract_canon_sha256":"c10089c152c83b7456f1baec5367eca4192fcde47175b3677460be6615c9db0b"},"schema_version":"1.0"},"canonical_sha256":"fe029081671b8d151af3ac389a1f79825aa9929f22667a70b481d399a80ea73d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:53:20.252258Z","signature_b64":"qQs+Ckr3N/Jl2WINib3Sk/nrU8kN1lBLVGlV3iI6WSAiuNnkI4zsYWjIWZgzOnRtO/ccTTAbU96bORN8+hFaCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe029081671b8d151af3ac389a1f79825aa9929f22667a70b481d399a80ea73d","last_reissued_at":"2026-07-05T03:53:20.251790Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:53:20.251790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2202.00264","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:53:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jc9JdqcHKfDI6+pyuwEajRUz09P94sxCWgDQA/uzTDVFMoGpNPhWlDkpkFwwZzLDJIJqhtuZQt+XCxwn+NdPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:46:08.713579Z"},"content_sha256":"4f1d1e275c0171b25064afe3aa43d428d88d1aad15662382500dc81dde3eb696","schema_version":"1.0","event_id":"sha256:4f1d1e275c0171b25064afe3aa43d428d88d1aad15662382500dc81dde3eb696"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7YBJBALHDOGRKGXTVQ4JUH3ZQJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA","math.NA","math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Jens Sj\\\"olund, Maria B{\\aa}nkestad","submitted_at":"2022-02-01T07:52:01Z","abstract_excerpt":"We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known in certain fields, e.g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations. We first consider low-rank factorization more broadly and propose a graph representation of the problem suited for graph neural networks. Then, we focus on the task of nonnegative matrix factorization and propose a graph neural network that interleaves bipartite self-attention layers"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00264","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.00264/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:53:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cg45hkxo/OyXoSgz5WHq6q4aLi2S9z2AQyN36GffwwuVwQh+QVcaITP29BKUYLO7Yc1P6mDBLoE3fhlRHzRCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:46:08.713953Z"},"content_sha256":"39a3403adb9b975c55d5522cd5dd05bf1b35a848bfe668374bbba0ff99fe72b1","schema_version":"1.0","event_id":"sha256:39a3403adb9b975c55d5522cd5dd05bf1b35a848bfe668374bbba0ff99fe72b1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/bundle.json","state_url":"https://pith.science/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T14:46:08Z","links":{"resolver":"https://pith.science/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ","bundle":"https://pith.science/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/bundle.json","state":"https://pith.science/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7YBJBALHDOGRKGXTVQ4JUH3ZQJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7YBJBALHDOGRKGXTVQ4JUH3ZQJ","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":"c10089c152c83b7456f1baec5367eca4192fcde47175b3677460be6615c9db0b","cross_cats_sorted":["cs.NA","math.NA","math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T07:52:01Z","title_canon_sha256":"5828c338e32dd292a8a6f49f3bac4af54e508141695b8dfdc3ee215a60b5b31b"},"schema_version":"1.0","source":{"id":"2202.00264","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.00264","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"arxiv_version","alias_value":"2202.00264v1","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.00264","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_12","alias_value":"7YBJBALHDOGR","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_16","alias_value":"7YBJBALHDOGRKGXT","created_at":"2026-07-05T03:53:20Z"},{"alias_kind":"pith_short_8","alias_value":"7YBJBALH","created_at":"2026-07-05T03:53:20Z"}],"graph_snapshots":[{"event_id":"sha256:39a3403adb9b975c55d5522cd5dd05bf1b35a848bfe668374bbba0ff99fe72b1","target":"graph","created_at":"2026-07-05T03:53:20Z","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/2202.00264/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We describe a graph-based neural acceleration technique for nonnegative matrix factorization that builds upon a connection between matrices and bipartite graphs that is well-known in certain fields, e.g., sparse linear algebra, but has not yet been exploited to design graph neural networks for matrix computations. We first consider low-rank factorization more broadly and propose a graph representation of the problem suited for graph neural networks. Then, we focus on the task of nonnegative matrix factorization and propose a graph neural network that interleaves bipartite self-attention layers","authors_text":"Jens Sj\\\"olund, Maria B{\\aa}nkestad","cross_cats":["cs.NA","math.NA","math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T07:52:01Z","title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.00264","kind":"arxiv","version":1},"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":"sha256:4f1d1e275c0171b25064afe3aa43d428d88d1aad15662382500dc81dde3eb696","target":"record","created_at":"2026-07-05T03:53:20Z","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":"c10089c152c83b7456f1baec5367eca4192fcde47175b3677460be6615c9db0b","cross_cats_sorted":["cs.NA","math.NA","math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-01T07:52:01Z","title_canon_sha256":"5828c338e32dd292a8a6f49f3bac4af54e508141695b8dfdc3ee215a60b5b31b"},"schema_version":"1.0","source":{"id":"2202.00264","kind":"arxiv","version":1}},"canonical_sha256":"fe029081671b8d151af3ac389a1f79825aa9929f22667a70b481d399a80ea73d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe029081671b8d151af3ac389a1f79825aa9929f22667a70b481d399a80ea73d","first_computed_at":"2026-07-05T03:53:20.251790Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:53:20.251790Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qQs+Ckr3N/Jl2WINib3Sk/nrU8kN1lBLVGlV3iI6WSAiuNnkI4zsYWjIWZgzOnRtO/ccTTAbU96bORN8+hFaCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:53:20.252258Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.00264","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f1d1e275c0171b25064afe3aa43d428d88d1aad15662382500dc81dde3eb696","sha256:39a3403adb9b975c55d5522cd5dd05bf1b35a848bfe668374bbba0ff99fe72b1"],"state_sha256":"de4c2ddb910d40d905a74428405324e41c24853f3edc95182fa310a8c9907b9e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SmR1tXa8eFErb0OhHYR9Ki1SdFJe3NqP0yhn5ViXMTwqeYYlPE7Z21v41xpFMFoEYhkH9vpRPVHeLKBApX4hAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:46:08.716178Z","bundle_sha256":"e377d5be4afb44a5af5362654ef51dd85f0fea8672c250f87972c8ef009632c0"}}