Community Detection in Degree-Corrected Block Models

Chao Gao; Zongming Ma; Anderson Y. Zhang; Harrison H. Zhou

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arXiv · 1607.06993

Community Detection in Degree-Corrected Block Models

Chao Gao·Zongming Ma·Anderson Y. Zhang·Harrison H. Zhou

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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.

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BibTeX RIS

Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou. 2016-07-24. Community Detection in Degree-Corrected Block Models. https://arxiv.org/abs/1607.06993

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