Community Detection Algorithms for Big Data using Graph Theory

Abstract

Community detection is a nowadays research problem in the Big Data era related to huge volume, variety, and velocity of data. Big data defines data where normal processing, storage, retrieval fails and require some advanced tools to solve these types of problem. An important tool in the analysis of complex network is community detection. Community detection or community mining is a technique which is used to find the same type of relations in a particular group. Community detection is also known as Graph Clustering. This paper represents Big data in the form of graphs and detects community via some graph algorithms like METIS, Spectral Partitioning, hierarchical clustering, Markov Clustering, Genetic Algorithm based community detection algorithm, etc. Community detection is widely used in various types of disease detection, drug formation, species clustering. It can be also used in social networking sites to control crimes by detecting community bad peoples.