Modeling dynamic social networks using concept of neighborhood theory

Abstract

Dynamic social network analysis basically deals with the study of how the nodes and edges and associations among them within the network alter with time, thereby forming a special category of social network. Geometrical analysis has been done on various occasions, but there is a difference in the approximate distances of nodes. Snapshots for social networks are taken at each time slot and then are bound for these studies. The paper will discuss an efficient way of modeling dynamic social networks with the concept of neighborhood theory of cellular automata. So far, no model that uses the concept of neighborhood has been proposed to the best of our knowledge and the literature survey. Besides cellular automata that has been important tool in various applications has remained unexplored in the area of modelling. To this extent the paper, is the 1st attempt in modelling the social network that is evolving in nature. A link prediction algorithm based on some basic graph theory concepts has also been additionally proposed for the emergence of new nodes within the network. Theoretical and programming simulations have been explained in support to the model. Finally, the paper will discuss the model with a real-life scenario.