B1752
Title: Community detection in degree-corrected block models
Authors: Anderson Zhang - University of Chicago (United States) [presenting]
Chao Gao - University of Chicago (United States)
Harrison Zhou - Yale University (United States)
Zongming Ma - University of Pennsylvania (United States)
Abstract: Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCBMs). We first derive asymptotic minimax risks of the problem for a misclassification proportion loss under appropriate conditions. The minimax risks are shown to depend on degree-correction parameters, community sizes, and average within and between community connectivities in an intuitive and interpretable way. In addition, we propose a polynomial time algorithm to adaptively perform consistent and even asymptotically optimal community detection in DCBMs.
Journal