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A Survey on Overlapping Communities in Large-Scale Social Networks
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Author(s): S Rao Chintalapudi (JNTUK, India)and H. M. Krishna Prasad M (JNTUK, India)
Copyright: 2018
Pages: 18
Source title:
Modern Technologies for Big Data Classification and Clustering
Source Author(s)/Editor(s): Hari Seetha (Vellore Institute of Technology-Andhra Pradesh, India), M. Narasimha Murty (Indian Institute of Science, India)and B. K. Tripathy (VIT University, India)
DOI: 10.4018/978-1-5225-2805-0.ch008
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Abstract
Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and biological networks and so on. The most important property of social networks is community, collection of nodes with dense connections inside and sparse connections at outside. Community detection is similar to clustering analysis and has many applications in the real-time world such as recommendation systems, target marketing and so on. Community detection algorithms are broadly classified into two categories. One is disjoint community detection algorithms and the other is overlapping community detection algorithms. This chapter reviews overlapping community detection algorithms with their strengths and limitations. To evaluate these algorithms, a popular synthetic network generator, i.e., LFR benchmark generator and the new extended quality measures are discussed in detail.
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