FAST COMMUNITY DETECTION BY SCORE

By Jiashun Jin. 2012

329 citations recorded by the source corpus.

communities. The community labels for the nodes are unknown and it is of major interest to estimate them (i.e., community detection). Degree Corrected Block Model (DCBM) is a popular network model. How to detect communities with the DCBM is an interesting problem, where the main challenge lies in the degree heterogeneity. We propose a new approach to community detection which we call the Spectral Clustering On Ratios-of-Eigenvectors (SCORE). Compared to classical spectral methods, the main innovation is to use the entry-wise ratios between the rst leading eigenvector and each of the other leading eigenvectors for clustering. Let X be the adjacency matrix of the network. We rst obtain the K leading eigenvectors, say, ^ 1;:::; ^ K, and let ^ R be then (K 1) matrix such that ^ R(i;k) = ^ k+1(i)=^ 1(i), 1 i n, 1 k K 1. We then use ^ R for clustering by applying the k-means method. The central surprise is, the eect of degree heterogeneity is largely ancillary, and can be eectively removed by taking entry-wise ratios between ^ k+1 and ^ 1, 1 k K 1. The method is successfully applied to the web blogs data and the karate club data, with error rates of 58=1222 and 1=34, respectively. These results are much more satisfactory than those by the classical spectral methods. Also, compared to modularity methods, SCORE is computationally much faster and has smaller error rates. We develop a theoretic framework where we show that under mild conditions, the SCORE stably yields successful community detection. In the core of the analysis is the recent development on Random Matrix Theory (RMT), where the matrix-form Bernstein inequality is especially helpful.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/270f9e25187071feb44e8e3eb28ece0a0a863e00

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Citations in the corpus

Consistency of spectral clustering in stochastic block models (2013, methodology): “A similar requirement of a polynomial growth of expected average degree is implicitly imposed in Jin (2012), who ﬁrst studied the performance of normalized k -means spectral clustering in degree corrected block models.”
Community detection and stochastic block models: recent developments (2017, background): “The results give about 95% agreement on the blogs’ political inclinations (which is roughly the state-of-the-art [133, 101, 80]).”
A goodness-of-fit test for stochastic block models (2014, methodology): “Most random matrix theory used in this paper (namely, Lemmas A.1, A.3, A.4) has been developed for moderately sparse stochastic block models with ρ n n − 1 / 3 in Erd˝os et al. (2013b, 2012).”