Influence minimization (IMIN) aims to identify a set of nodes to be blocked, such that the expected number of nodes activated by the given seed set is minimized. It has many important applications, such as misinformation suppression, and has been extensively studied in the literature. Existing works for IMIN, however, neglect key temporal information in real-world scenarios. In this paper, we generalize IMIN and study the time-critical influence minimization (TCIM) problem, which aims to minimize the activation duration-aware influence spread of the seed set by a deadline via node blocking. We show that TCIM is NP-hard and APX-hard, and the objective function is non-submodular. To address the problem, we propose CBFM, an efficient and effective algorithm that provides τ(1-1/e-ε)-approximation with at least 1-3δ probability, where τ is a data-driven parameter, ε and δ are tunable error parameters. Novel concentration results are designed to facilitate the establishment of the approximation guarantee. Moreover, we show that CBFM can be extended to tackle the misinformation mitigation (MM) problem. The existing MM solution offers the approximation guarantee only under specific assumptions. Our extended approach is assumption-free yet still attains the same guarantee, thereby bridging the theoretical gap. Finally, we conduct extensive experiments on 11 datasets to validate the performance of proposed algorithms on TCIM, IMIN (a special case of TCIM), and MM problems. The results show that for TCIM, CBFM achieves up to four orders of magnitude speedup over the baseline; for IMIN, CBFM outperforms the state-of-the-art in terms of efficiency, approximation ratio, and memory usage. Moreover, for MM, our solution can be two orders of magnitude faster than the corresponding state-of-the-art.
Wang et al. (Thu,) studied this question.