Efficient Influence Maximization in Social-Networks Under Independent Cascade Model

Abstract

Abstract With in a social network, users interact with each other by sharing news, picture, videos, political content or product promotion content. This makes social networks information diffusion(or marketing) platform. To realize the extent of the adoption of such ideas, it becomes important to study the dynamics of adoption underlying within the social network. However, to analyze social networks as information diffusion/marketing platform, many challenges have to be met. In this paper, we address one of the challenge, namely Influence Maximization in Social Networks. Our main purpose is to select k influential users which can gain maximum spread while cost of selecting k users is minimum. In this paper, a) We have proposed an degree heuristic algorithm under Independent Cascade Model (ICM) to extract top k influential users efficiently. b) A modification in ICM is proposed which derives propagation probability based on similarity metrics. The proposed work is implemented and evaluated on two network data- sets, academic collaboration is taken from the online archival database arXiv.org. The obtained results prove that degree heuristic algorithm is very efficient and has influence spread far better than many centrality based heuristics and close to benchmark greedy algorithm. On the other hand, modification in ICM has a significant stable increase in the influence achieved for implemented algorithms compared to influence achieved by the same algorithms on classic ICM.