{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WZRDT6I7TZ3Z47UOR2QK7QDHX6","short_pith_number":"pith:WZRDT6I7","schema_version":"1.0","canonical_sha256":"b66239f91f9e779e7e8e8ea0afc067bfbfb717a67a8f598cc3b4eb19d415a821","source":{"kind":"arxiv","id":"1807.00145","version":1},"attestation_state":"computed","paper":{"title":"Sampling and Reconstruction of Signals on Product Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"eess.SP","authors_text":"Geert Leus, Guillermo Ortiz-Jim\\'enez, Mario Coutino, Sundeep Prabhakar Chepuri","submitted_at":"2018-06-30T08:55:58Z","abstract_excerpt":"In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experimen"},"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":"1807.00145","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-06-30T08:55:58Z","cross_cats_sorted":["cs.IT","math.IT"],"title_canon_sha256":"0133df7c40094c26d6c5a866dd25f50f600827ade6c438ebda6ea5ed8734922e","abstract_canon_sha256":"e0111b99b73d619e793e13caba4cc0899002d1ef608e00faaa86f258df9499cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:55.928931Z","signature_b64":"BfKiLb1vq/HyK2zZNmFDDrFCZZONv28fxRFND4FnvxovOA56uXaA4ocel9QtZEc1wFPNWjoHLncZiZKEkVpaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b66239f91f9e779e7e8e8ea0afc067bfbfb717a67a8f598cc3b4eb19d415a821","last_reissued_at":"2026-05-18T00:11:55.928188Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:55.928188Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sampling and Reconstruction of Signals on Product Graphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT"],"primary_cat":"eess.SP","authors_text":"Geert Leus, Guillermo Ortiz-Jim\\'enez, Mario Coutino, Sundeep Prabhakar Chepuri","submitted_at":"2018-06-30T08:55:58Z","abstract_excerpt":"In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experimen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00145","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":"1807.00145","created_at":"2026-05-18T00:11:55.928330+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00145v1","created_at":"2026-05-18T00:11:55.928330+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00145","created_at":"2026-05-18T00:11:55.928330+00:00"},{"alias_kind":"pith_short_12","alias_value":"WZRDT6I7TZ3Z","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WZRDT6I7TZ3Z47UO","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WZRDT6I7","created_at":"2026-05-18T12:33:01.666342+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/WZRDT6I7TZ3Z47UOR2QK7QDHX6","json":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6.json","graph_json":"https://pith.science/api/pith-number/WZRDT6I7TZ3Z47UOR2QK7QDHX6/graph.json","events_json":"https://pith.science/api/pith-number/WZRDT6I7TZ3Z47UOR2QK7QDHX6/events.json","paper":"https://pith.science/paper/WZRDT6I7"},"agent_actions":{"view_html":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6","download_json":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6.json","view_paper":"https://pith.science/paper/WZRDT6I7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00145&json=true","fetch_graph":"https://pith.science/api/pith-number/WZRDT6I7TZ3Z47UOR2QK7QDHX6/graph.json","fetch_events":"https://pith.science/api/pith-number/WZRDT6I7TZ3Z47UOR2QK7QDHX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6/action/storage_attestation","attest_author":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6/action/author_attestation","sign_citation":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6/action/citation_signature","submit_replication":"https://pith.science/pith/WZRDT6I7TZ3Z47UOR2QK7QDHX6/action/replication_record"}},"created_at":"2026-05-18T00:11:55.928330+00:00","updated_at":"2026-05-18T00:11:55.928330+00:00"}