Overlapping community detection using Bayesian non-negative matrix factorization

被引：278

作者：

Psorakis, Ioannis ^{[1
]}

Roberts, Stephen ^{[1
]}

Ebden, Mark ^{[1
]}

Sheldon, Ben ^{[2
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Pattern Anal & Machine Learning Res Grp, Oxford OX1 3PJ, England

[2] Univ Oxford, Dept Zool, Edward Grey Inst, Oxford OX1 3PS, England

来源：

PHYSICAL REVIEW E | 2011年 / 83卷 / 06期

基金：

英国工程与自然科学研究理事会;

关键词：

INDEPENDENT COMPONENT ANALYSIS; COMPLEX NETWORKS;

D O I：

10.1103/PhysRevE.83.066114

中图分类号：

O35 [流体力学]; O53 [等离子体物理学];

学科分类号：

070204 ; 080103 ; 080704 ;

摘要：

Identifying overlapping communities in networks is a challenging task. In this work we present a probabilistic approach to community detection that utilizes a Bayesian non-negative matrix factorization model to extract overlapping modules from a network. The scheme has the advantage of soft-partitioning solutions, assignment of node participation scores to modules, and an intuitive foundation. We present the performance of the method against a variety of benchmark problems and compare and contrast it to several other algorithms for community detection.

引用

页数：9

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