A Novel Approach for Detecting Relationships in Social Networks Using Cellular Automata Based Graph Coloring

Abstract

All the social networks can be modeled as a graph, where each roles as vertex and each relation<br />roles as an edge. The graph can be show as G = [V;E], where V is the set of vertices and E is the<br />set of edges. All social networks can be segmented to K groups, where there are members in each<br />group with same features. In each group each person knows other individuals and is in touch with<br />them. In this study, the main goal is introducing a new approach for detecting these groups and<br />minimizing the number of these groups using a cellular automat algorithm. There are two types of<br />social networks, containing simulated social network and real social network. The results show that<br />the introduced method has a great potential to signi cantly reduce the number of colors assigned<br />and running time of the program.