{"id":"b6190b86-fefc-4339-b95b-e806fed896df","arxiv_id":"1908.00388","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"A new greedy sampling method, TCEC, uses spectral projection bounds to estimate eigenvector centrality rankings on incomplete networks and outperforms random-walk baselines on several real-world networks.","lead":"This paper presents TCEC, a new sampling algorithm that estimates eigenvector centrality rankings from a small subsample of a network by greedily adding nodes that reduce a spectral error bound. It matters because it offers a theoretically motivated alternative to random-walk and uniform sampling for large networks where data collection is expensive or partial.","discovery_kind":"new_method","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-14T15:58:32.355189+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}