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We find in simulations that when clusters have low conductance, cluster\u2010robust methods control size better than HAC estimators. However, for important classes of networks lacking low\u2010conductance clusters, the former can exhibit substantial size distortion. To determine the number of low\u2010conductance clusters and construct them, we draw on results in spectral graph theory that connect conductance to the spectrum of the graph Laplacian. Based on these results, we propose to use the spectrum to determine the number of low\u2010conductance clusters and spectral clustering to construct them.<\/jats:p>","DOI":"10.3982\/ecta19816","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T11:48:34Z","timestamp":1678708114000},"page":"641-667","source":"Crossref","is-referenced-by-count":14,"title":["Network Cluster\u2010Robust Inference"],"prefix":"10.3982","volume":"91","author":[{"given":"Michael P.","family":"Leung","sequence":"first","affiliation":[{"name":"Department of Economics, UC Santa Cruz"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2359","published-online":{"date-parts":[[2023]]},"reference":[{"issue":"1","key":"ecta200562-bib-0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms14753","volume":"8","author":"Aral","year":"2017","journal-title":"Nature 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