Community Detection in Degree-Corrected Block Models
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.
Keywords
Cite
@article{arxiv.1607.06993,
title = {Community Detection in Degree-Corrected Block Models},
author = {Chao Gao and Zongming Ma and Anderson Y. Zhang and Harrison H. Zhou},
journal= {arXiv preprint arXiv:1607.06993},
year = {2016}
}