Maximizing the spread of influence ranking in social networks

Information flows in a network where individuals influence each other. In this paper, we study the influence maximization problem of finding a small subset of nodes in a social network that could maximize the spread of influence. We propose a novel information diffusion model CTMC-ICMCTMC-ICM, which introduces the theory of Continuous-Time Markov Chain (CTMC) into the Independent Cascade Model (ICM). Furthermore, we propose a new ranking metric named SpreadRank generalized by the new information propagation model CTMC-ICM. We experimentally demonstrate the new ranking method that can, in general, extract nontrivial nodes as an influential node set that maximizes the spread of information in a social network and is more efficient than a distance-based centrality.