Node deletion-based algorithm for blocking maximizing on negative influence from uncertain sources

The spreading of negative influence, such as epidemic, rumor, false information and computer virus, may lead to serious consequences in social networks. The issue of negative influence blocking maximization arouses intense interest of the researchers. However, in the real world social network environment, the exact source of negative influence is usually unknown. In most cases, we only know the distribution of negative seeds, which is the probability for each node to be a negative seed. In this work, we investigate the problem of maximizing the blocking on negative influence from uncertain sources. We propose the competitive influence linear threshold propagation model (CI-LTPM) for the problem. Based on the IC-LTPM model, we define the problem of uncertain negative source influence blocking maximization (UNS-IBM). We use the propagation tree in the live-edge (LE) sub-graph for estimating the influence propagation. An algorithm is proposed to calculate the blocking increments of the positive seeds based on the propagation tree in the LE sub-graph. We observed that the blocking effect of the positive seeds is the reduction on the negative influence after the positive seeds and their related edges being deleted from the LE sub-graph. Based on such observation, we propose a node deletion-based algorithm NDB (node-deletion-blocking) for solving the UNS-IBM problem. Our experiment results show that NDB can block more negative influence in less computational time than other methods.