In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set \ud835\udc46, the problem is to minimize the influence spread from \ud835\udc46 by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking virus (e.g. COVID-19) propagation in social networks by quarantine and vaccination, blocking rumor spread by freezing fake accounts, or attacking competitor\u2019s influence by incentivizing some users to ignore the information from the competitor. In this paper, under the linear threshold model, we adapt the reverse influence sampling approach and provide efficient algorithms of sampling valid reverse reachable paths to solve the problem. We present three different design choices on reverse sampling, which all guarantee 1\/2 \u2212\ud835\udf00 approximation (for any small \ud835\udf00 > 0) and an efficient running time.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we study the adversarial attacks on influence maximization under dynamic influence propagation models in social networks. In particular, given a known seed set \ud835\udc46, the problem is to minimize the influence spread from \ud835\udc46 by deleting a limited number of nodes and edges. This problem reflects many application scenarios, such as blocking 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