{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:REX7URBCGW5Q5R2H5IX52VGX4E","short_pith_number":"pith:REX7URBC","schema_version":"1.0","canonical_sha256":"892ffa442235bb0ec747ea2fdd54d7e13e16e0429c4b08850911c755f77e54df","source":{"kind":"arxiv","id":"1709.03726","version":1},"attestation_state":"computed","paper":{"title":"Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"cs.LG","authors_text":"Elvin Isufi, Geert Leus, Paolo Banelli, Paolo Di Lorenzo, Sergio Barbarossa","submitted_at":"2017-09-12T08:06:22Z","abstract_excerpt":"The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed "},"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":"1709.03726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-09-12T08:06:22Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"f13ee5b9635b857036b1df7a5c4a708abbaa2eb18f3c874a68b4e29f9b439b19","abstract_canon_sha256":"88cf105c363408ba253f0d703281b382664e4ea57f7244829c042727c0552101"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:30.815049Z","signature_b64":"Oh4ci5Hb6RcBqjJqIQSb3IdbHoucrhc1uaSFAC+CnfUmL00lMxq4J/pibklxSp1FcTA6EwI8ZIiVuIe6U5ioBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"892ffa442235bb0ec747ea2fdd54d7e13e16e0429c4b08850911c755f77e54df","last_reissued_at":"2026-05-18T00:09:30.814484Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:30.814484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"cs.LG","authors_text":"Elvin Isufi, Geert Leus, Paolo Banelli, Paolo Di Lorenzo, Sergio Barbarossa","submitted_at":"2017-09-12T08:06:22Z","abstract_excerpt":"The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1709.03726","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":"1709.03726","created_at":"2026-05-18T00:09:30.814568+00:00"},{"alias_kind":"arxiv_version","alias_value":"1709.03726v1","created_at":"2026-05-18T00:09:30.814568+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1709.03726","created_at":"2026-05-18T00:09:30.814568+00:00"},{"alias_kind":"pith_short_12","alias_value":"REX7URBCGW5Q","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_16","alias_value":"REX7URBCGW5Q5R2H","created_at":"2026-05-18T12:31:39.905425+00:00"},{"alias_kind":"pith_short_8","alias_value":"REX7URBC","created_at":"2026-05-18T12:31:39.905425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.10471","citing_title":"State-Space Network Topology Identification from Partial Observations","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E","json":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E.json","graph_json":"https://pith.science/api/pith-number/REX7URBCGW5Q5R2H5IX52VGX4E/graph.json","events_json":"https://pith.science/api/pith-number/REX7URBCGW5Q5R2H5IX52VGX4E/events.json","paper":"https://pith.science/paper/REX7URBC"},"agent_actions":{"view_html":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E","download_json":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E.json","view_paper":"https://pith.science/paper/REX7URBC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1709.03726&json=true","fetch_graph":"https://pith.science/api/pith-number/REX7URBCGW5Q5R2H5IX52VGX4E/graph.json","fetch_events":"https://pith.science/api/pith-number/REX7URBCGW5Q5R2H5IX52VGX4E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E/action/storage_attestation","attest_author":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E/action/author_attestation","sign_citation":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E/action/citation_signature","submit_replication":"https://pith.science/pith/REX7URBCGW5Q5R2H5IX52VGX4E/action/replication_record"}},"created_at":"2026-05-18T00:09:30.814568+00:00","updated_at":"2026-05-18T00:09:30.814568+00:00"}