Aspects of Various Community Detection Algorithms in Social Network Analysis

Aspects of Various Community Detection Algorithms in Social Network Analysis

Nicole Belinda Dillen (St. Thomas' College of Engineering and Technology, India) and Aruna Chakraborty (St. Thomas' College of Engineering and Technology, India)
DOI: 10.4018/978-1-5225-7601-3.ch026

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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Community Detection Algorithms: New And Old

Traditional View of Communities in Social Networks

Most community detection algorithms consider a model in which each individual in a network belongs to a single community. The individuals in this community will have many connections with each other but will have a minimal number of connections to individuals belonging to other communities. In fact, this very phenomenon is exploited by nearly all of the community detection algorithms prevalent in social network analysis.

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