{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T13:39:14Z","timestamp":1787060354852,"version":"build-2736575974"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T00:00:00Z","timestamp":1569801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000185","name":"Defense Advanced Research Projects Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute for Computational Medicine"},{"name":"Kavli Neuroscience Discovery Institute"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.<\/jats:p>","DOI":"10.1093\/biomet\/asz045","type":"journal-article","created":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T15:15:29Z","timestamp":1566314129000},"page":"857-873","source":"Crossref","is-referenced-by-count":16,"title":["Network dependence testing via diffusion maps and distance-based correlations"],"prefix":"10.1093","volume":"106","author":[{"given":"Youjin","family":"Lee","sequence":"first","affiliation":[{"name":"Center for Causal Inference, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cencheng","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Applied Economics and Statistics, University of Delaware, Newark, Delaware, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carey E","family":"Priebe","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. 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