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Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a littleunderstood on how well we can approximate the maximum...
In this work, we introduce two approaches, namely iSNMF and iANMF, for effectively identifying social communities using Nonnegative Matrix Factorization (NMF) with I-divergence as the cost function. Our approaches work by iteratively factorizing the nonnegative input matrix through derived multiplicative update rules. By doing so, we can not only extract meaningful overlapping communities via soft...
Social networks exhibit a very special property: community structure. Understanding the network community structure is of great advantages. It not only provides helpful information in developing more social-aware strategies for social network problems but also promises a wide range of applications enabled by mobile networking, such as routings in Mobile Ad Hoc Networks (MANETs) and worm containments...
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