Social networks as a domain of complex networks that can be represented as graphs according to the patterns of connections among their elements. Social Communities are a set of nodes with denser connections inside community structures than outside. The goal of graph clustering is to divide the large graph into many clusters depending on multiple similarity criteria. In this work an improved version of the Louvain method is proposed, the Greedy Modularity Graph Clustering for Community Detection of Large Co-AuthorshipNetwork (GMGC)which introduces a new concept of weighted edges to enhance the accuracy of the Community Discovery for the large networks. The method is compared with other states of art methods mainly, Vertices Similarity First and Community Mean (VSFCM), and Generalized Louvain method for community detection in large networks (FKCD). Extensive experimental results have been madeon different datasets. The experimental results showed that the proposed method outperforms the other states of arts comparative methods according to the modularity optimization and community partitions evaluations measures.

Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.