Clustering Social Networks to Remove Neutral Nodes

Multi agent systems with autonomous interaction, negotiation and learning capabilities can efficiently model social behavior of individuals participating in a social network. A central problem in a social network is to identify the nodes that actively participate in the expansion of the net both physically and functionally. Several metrics have already been proposed to identify those hot spots. The algorithms to identify hot spots are either heuristic based or computationally expensive. In this paper we use an agent model of the social net and propose a method that can identify the neutral nodes, i.e. the nodes that can never be considered as hot spot nodes given the network topology and rules of negotiation among nodes. Therefore these nodes can be eliminated from the net. A direct advantage of this method is reducing the computational complexity for the configuration and identification of hot spots. Through a case study we have shown that the proposed method can lead to 33% reduction of computation regarding the number of agent types in the example.