Seed Selection Algorithm using Centrality based Entropy

Abstract

In the field of research in social networks, the influence maximization problem has captured an abundance of attention. Mostly, influence maximization is performed by selecting the best suitable seed nodes from the network to initiate the diffusion process. By following the seed nodes, other nodes get in touch with the information spread by seed nodes. Still, the seed selection problem is NP-hard problem. But there may be a selection algorithm through which optimal diffusion can be achieved. In this paper we proposed the seed selection method to improve the diffusion speed and performance using existing centrality measures. To validate the efficiency of the proposed algorithm, we also have conducted a comparison study of proposed seed selection algorithms with existing benchmark algorithms. To evaluate the proposed algorithm, we have used real-world authors collaboration networks.