摘要
The problem of influence maximization in the social network G is to find k seed nodes with the maximum influence.The seed set S has a wider range of influence in the social network G than other same-size node sets.The influence of a node is usually established by using the IC model(Independent Cascade model)with a considerable amount of Monte Carlo simulations used to approximate the influence of the node.In addition,an approximate effect(11=e)is obtained,when the number of Monte Carlo simulations is 10000 and the probability of propagation is very small.In this paper,we analyze that the propagative range of influence of node set is limited in the IC model,and we find that the influence of node only spread to the t0-th neighbor.Therefore,we propose a greedy algorithm based on the improved IC model that we only consider the influence in the t0-th neighbor of node.Finally,we perform experiments on 10 real social network and achieve favorable results.
基金
This research was supported in part by the Chinese National Natural Science Foundation under grant Nos.61602202 and 61702441
the Natural Science Foundation of Jiangsu Province under contracts BK20160428 and BK20161302
the Six talent peaks project in Jiangsu Province under contract XYDXX-034 and the project in Jiangsu Association for science and technology.
