{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CDWQUDOG4DMHW7H6DX4FTOJ3SB","short_pith_number":"pith:CDWQUDOG","canonical_record":{"source":{"id":"2410.15716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-21T07:34:17Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"88a048d79362fe2b30ce29b9ca1a00e9c7f28fcc1d62e1b93c422e4deb310ada","abstract_canon_sha256":"c7f66086fbf08b528ddd02192222be6c0c167609858f98ba1009247e72fab909"},"schema_version":"1.0"},"canonical_sha256":"10ed0a0dc6e0d87b7cfe1df859b93b907b564018895e7a7939818f9480d2c37f","source":{"kind":"arxiv","id":"2410.15716","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15716","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15716v1","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15716","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_12","alias_value":"CDWQUDOG4DMH","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_16","alias_value":"CDWQUDOG4DMHW7H6","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_8","alias_value":"CDWQUDOG","created_at":"2026-07-05T09:23:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CDWQUDOG4DMHW7H6DX4FTOJ3SB","target":"record","payload":{"canonical_record":{"source":{"id":"2410.15716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-21T07:34:17Z","cross_cats_sorted":["cs.NI"],"title_canon_sha256":"88a048d79362fe2b30ce29b9ca1a00e9c7f28fcc1d62e1b93c422e4deb310ada","abstract_canon_sha256":"c7f66086fbf08b528ddd02192222be6c0c167609858f98ba1009247e72fab909"},"schema_version":"1.0"},"canonical_sha256":"10ed0a0dc6e0d87b7cfe1df859b93b907b564018895e7a7939818f9480d2c37f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:23:23.703123Z","signature_b64":"f6VINE7sp9p5qBn6V9AcEK2jmreK5MFjpJhUyzR4O608fi7DpPLgMpUem6rhfYuJ6OPgGTc3S8Fpt35aiGqHCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"10ed0a0dc6e0d87b7cfe1df859b93b907b564018895e7a7939818f9480d2c37f","last_reissued_at":"2026-07-05T09:23:23.702617Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:23:23.702617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.15716","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":"2026-07-05T09:23:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tReRSdyYSQ6vB/4ETWsSAyOENAOOeEsRjJa6+okPhC8gd0TLMhadY1idyEb2muFA3x7iL5GCxcollpV/ka/oDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T15:25:34.840225Z"},"content_sha256":"8573af3cffa7c2f18bfacf76249c9067692441afa8e5f4c7d83701dbd61cbcfe","schema_version":"1.0","event_id":"sha256:8573af3cffa7c2f18bfacf76249c9067692441afa8e5f4c7d83701dbd61cbcfe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CDWQUDOG4DMHW7H6DX4FTOJ3SB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NI"],"primary_cat":"cs.LG","authors_text":"Benchu Zhang, Pei Zhao, Rongyao Hu, Xinyu Yuan, Yan Qiao","submitted_at":"2024-10-21T07:34:17Z","abstract_excerpt":"The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15716","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/2410.15716/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":"2026-07-05T09:23:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XctEXj83vbXlgHs7a2sji3orvaNue7eqvOtMq5CL/hrwC1lx1Uwmjz/ghEA6lzltZu3tZsynEohBnUqfjWFGCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T15:25:34.840819Z"},"content_sha256":"3245b37c992014eceadf4c3d811e8f1c7c9fb104f521c4c041d44d4bd22380fe","schema_version":"1.0","event_id":"sha256:3245b37c992014eceadf4c3d811e8f1c7c9fb104f521c4c041d44d4bd22380fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/bundle.json","state_url":"https://pith.science/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/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":"2026-08-22T15:25:34Z","links":{"resolver":"https://pith.science/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB","bundle":"https://pith.science/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/bundle.json","state":"https://pith.science/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CDWQUDOG4DMHW7H6DX4FTOJ3SB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CDWQUDOG4DMHW7H6DX4FTOJ3SB","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":"c7f66086fbf08b528ddd02192222be6c0c167609858f98ba1009247e72fab909","cross_cats_sorted":["cs.NI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-21T07:34:17Z","title_canon_sha256":"88a048d79362fe2b30ce29b9ca1a00e9c7f28fcc1d62e1b93c422e4deb310ada"},"schema_version":"1.0","source":{"id":"2410.15716","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.15716","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"arxiv_version","alias_value":"2410.15716v1","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.15716","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_12","alias_value":"CDWQUDOG4DMH","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_16","alias_value":"CDWQUDOG4DMHW7H6","created_at":"2026-07-05T09:23:23Z"},{"alias_kind":"pith_short_8","alias_value":"CDWQUDOG","created_at":"2026-07-05T09:23:23Z"}],"graph_snapshots":[{"event_id":"sha256:3245b37c992014eceadf4c3d811e8f1c7c9fb104f521c4c041d44d4bd22380fe","target":"graph","created_at":"2026-07-05T09:23:23Z","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/2410.15716/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the e","authors_text":"Benchu Zhang, Pei Zhao, Rongyao Hu, Xinyu Yuan, Yan Qiao","cross_cats":["cs.NI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-21T07:34:17Z","title":"Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.15716","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":"sha256:8573af3cffa7c2f18bfacf76249c9067692441afa8e5f4c7d83701dbd61cbcfe","target":"record","created_at":"2026-07-05T09:23:23Z","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":"c7f66086fbf08b528ddd02192222be6c0c167609858f98ba1009247e72fab909","cross_cats_sorted":["cs.NI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-21T07:34:17Z","title_canon_sha256":"88a048d79362fe2b30ce29b9ca1a00e9c7f28fcc1d62e1b93c422e4deb310ada"},"schema_version":"1.0","source":{"id":"2410.15716","kind":"arxiv","version":1}},"canonical_sha256":"10ed0a0dc6e0d87b7cfe1df859b93b907b564018895e7a7939818f9480d2c37f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"10ed0a0dc6e0d87b7cfe1df859b93b907b564018895e7a7939818f9480d2c37f","first_computed_at":"2026-07-05T09:23:23.702617Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:23:23.702617Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"f6VINE7sp9p5qBn6V9AcEK2jmreK5MFjpJhUyzR4O608fi7DpPLgMpUem6rhfYuJ6OPgGTc3S8Fpt35aiGqHCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:23:23.703123Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.15716","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8573af3cffa7c2f18bfacf76249c9067692441afa8e5f4c7d83701dbd61cbcfe","sha256:3245b37c992014eceadf4c3d811e8f1c7c9fb104f521c4c041d44d4bd22380fe"],"state_sha256":"a33dbbb5be827820ee64a793395ae3579d3f64ca6456c00ad8116fe7a48a9030"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bo+SPNil81QMrGaK0Hy4n0JacXUA0f4GI6MtAb2mnine5oNiU+Wow+iZSaFl8uBQUp6x16Gya/nstg/X0547Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T15:25:34.845682Z","bundle_sha256":"b4905bf2067293948218b7a5a6a6dd9935a86a570c586e4d4851b6d8bfb8a5c8"}}