Improvement of the Recognition of Relationships in Social Networks Using Complementary Graph Coloring Based on Cellular Automata

Each social network can be modeled as a graph G = [V, E] in which V is a vertex representing a person in this social network, and E is an edge representing the existence of a relationship between two individuals. The social infrastructure with size m is known as the Km group in the social network G with m individual. In other words, in a Km, each person knows other individuals and is in touch with them. The present study aims at developing a method for optimizing interpersonal communication in the social network using a simple cellular automaton algorithm. The experimental results obtained from both simulated social network and two real social networks were analyzed. The findings revealed that the proposed method has the potential to considerably reduce not only the number of colors assigned but also the running time of the program.