Community detection of political blogs network based on structure-attribute graph clustering model

Abstract

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.

Strategies for improving the quality of community detection based on modularity optimization

“…Research to develop community detection methods and algorithms is growing rapidly, in line with the needs of applications that are increasingly broad and complex in the real world. In addition to social networks, several examples of the application of community detection are in various fields, including criminal [2], public health [3], politics [4], library [5], and prediction [6], [7]. Many interdisciplinary researchers have attempted to solve this problem.…”