A Comparison of Community Detection In Social Network Using Modularity

Abstract

Social network analysis has undergone a renaissance with the ubiquity and quantity of content from social media, web pages, and sensors. This content is a rich data source for constructing and analyzing social networks. This paper is addressing the problems in constructing the community structure of a networks. Graph analytics have proven to be valuable tools in solving this challenges. Network become an intensive subject of research for example in computer science, networking, network sciences etc., a growing need for valid and useful dataset is presented. Useful ways of addressing this problem are sampling based on the nodes (user)ids in the social network until sufficient amount of data has been obtained. This paper is presented the community detection algorithm such as modularity methods. Then compare the given dataset Vs different data set.