  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 S, the problem is to minimize the influence spread from
S 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's 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 $1/2 - \varepsilon$ approximation (for any small
$\varepsilon >0$) and an efficient running time.
