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Aspects of Various Community Detection Algorithms in Social Network Analysis

Aspects of Various Community Detection Algorithms in Social Network Analysis
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Author(s): Nicole Belinda Dillen (St. Thomas' College of Engineering and Technology, India) and Aruna Chakraborty (St. Thomas' College of Engineering and Technology, India)
Copyright: 2018
Pages: 12
Source title: Encyclopedia of Information Science and Technology, Fourth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-2255-3.ch603

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Abstract

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning and, in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

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