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DTSTART:20190331T010000
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DTSTART:20191027T010000
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DTSTART;TZID=Europe/Paris:20190624T103000
DTEND;TZID=Europe/Paris:20190624T114500
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SUMMARY:Zheng Tracy KE (Harvard University) - "Optimal Adaptivity of Signed-Polygon Statistics for Network Testing"
DESCRIPTION:\nThe Statistical Seminar: \nTime: 10:30 am – 11:45 am\nDate: 24th of June 2019\nPlace: Room 3105.\nZheng Tracy KE (Harvard University) – “Optimal Adaptivity of Signed-Polygon Statistics for Network Testing“ \nAbstract:Given a symmetric social network\, we are interested in testing whether it has only one community or multiple communities. The desired tests should (a) accommodate severe degree heterogeneity\, (b) accommodate mixed-memberships\, (c) have a tractable null distribution\, and (d) adapt automatically to different levels of sparsity\, and achieve the optimal phase diagram. How to find such a test is a challenging problem. \nWe propose the Signed Polygon as a class of new tests. Fixing m ≥ 3\, for each m-gon in the network\, define a score using the centered adjacency matrix. The sum of such scores is then the m-th order Signed Polygon statistic. The Signed Triangle (SgnT) and the Signed Quadrilateral (SgnQ) are special examples of the Signed Polygon. We show that both the SgnT and SgnQ tests satisfy (a)-(d)\, and especially\, they work well for both very sparse and less sparse networks. Our proposed tests compare favorably with the existing tests. For example\, the EZ and GC tests behave unsatisfactorily in the less sparse case and do not achieve the optimal phase diagram. Also\, many existing tests do not allow for severe heterogeneity or mixed-memberships\, and they behave unsatisfactorily in our settings. \nThe analysis of the SgnT and SgnQ tests is delicate and tedious\, and the main reason is that we need a unified proof that covers a wide range of sparsity levels and a wide range of degree heterogeneity. For lower bound theory\, we use a phase transition framework\, which includes the standard minimax argument\, but is more informative. The proof uses classical theorems on matrix scaling.\n(Joint work with Jiashun Jin and Shengming Luo. arXiv preprint: https://arxiv.org/abs/1904.09532)\nOrganizers:\nCristina BUTUCEA\, Alexandre TSYBAKOV\, Julie JOSSE\, Eric MOULINES\, Mathieu ROSENBAUM\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/zheng-tracy-ke/
CATEGORIES:Statistics
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