A STUDY AND ANALYSIS OF VARIOUS COMMUNITY DETECTION TECHNIQUES IN LARGE AND COMPLEX NETWORKS

Abstract

Real world complex networks such as social networks, biological networks usually exhibit in homogeneity, resulting in densely interconnected nodes, communities, which play an important functional role in the original system. Analyzing such communities in large networks has rapidly become one of the major topics in complex networks. Complex systems are composed of a large number of interacting elements such that the system as a whole exhibits emergent properties not obvious from the properties of its individual parts. Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. Detecting such communities in large networks has rapidly become one of the focal topics in the science of complex networks. The challenge in community detection is to define what constitutes a community in such a way that this definition not only yields meaningful communities but also allows for sufficiently fast algorithmic implementation to find them. In particular, identifying communities in large-complex networks is an important task in many scientific domains. In this review, we evaluated state-of-the-art and traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. In this paper, we study a focused review of different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the various purposes