Abstract:
The Influence Maximization algorithm based on Information Entropy in Heterogeneous social networks (IMIEH) is proposed in this paper. Firstly, the participation entropy and interaction entropy of nodes are proposed due to the different information carried and transmitted by different types of nodes, further the influence weight between nodes is presented. Then, based on the linear threshold model, the global influence of nodes is defined. Finally, the seed set is computed according to the marginal gain of nodes. Experimental results show that compared with MPIE, SimPath and DAGIM algorithms, the seed set obtained by the proposed algorithm IMIEH has a larger influence range.