COMMUNITY DETECTION IN SOCIAL NETWORK USING GRAPH CLUSTERING METHODS - SURYANENI PRIYANKA, SAI RAMA KRISHNA (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Community detection is a major analysis area in social media analysis where we identify the social network construction. Detecting communities is of main interest in sociology, computer science, biology, and methods, where systems are usually described as graphs. Community detection aims at discovery clusters as subgraphs within a specified network. A community is then a cluster where many edges link nodes of the same group and few edges link nodes of different clusters. With the democratization of the internet, communicating and sharing information is more manageable than ever. Community detection is a solution to understanding the structure of complex networks and finally extracting useful information from them. In Facebook, the existence of communities (groups) is a critical question; thus, many researchers focus on potential communities by using techniques like data mining and web mining. In this paper, the community detection for a Facebook social network is presenting.Ithas developed in network science to find groups within complex systems depicted on a graph.The proposed model divided into various phases such as a) sub-graph discovery, b) vertex clustering, c) community quality optimization, d) divisive, and e) model-based. For community detection defining the consistency between social particles, Social media applied Social Balance, Social status theory, Social correlation theory, and finally applying K-means clustering over facebook data set. The Experimental conducted on Face book social media dataset with multiple edges and nodes. The experimental results shows that the proposed model gets higher accuracy in community detection compared with state of the art methods.