Maximization influence in dynamic social networks and graphs

Social influence and influence diffusion have been extensively studied in social networks. However, most existing works on influence diffusion focus on static networks. In this paper, we study the problem of maximizing influence diffusion in dynamic social networks, i.e. networks that change over time. We propose the following algorithms under the Linear Threshold (LT) and Independent Cascade (IC) models: (a) the DM algorithm which is an extension of MATI algorithm and solves the Influence Maximization (IM) problem in dynamic networks, (b) the DM-C algorithm which is a latter version of DM and solves the IM problem using k-core decomposition and the core number information, (c) the DM-T algorithm which is another version of DM, that uses K-truss decomposition and the truss number information in order to solve the IM problem. Experimental results show that our proposed algorithms increase diffusion performance by 2 times compared with several state of the art algorithms and achieve comparable results in diffusion with the Greedy algorithm. Also, the proposed algorithms are 8.5 times faster in computational time compared with previous methods.