Online misinformation has been considered as one of the top global risks as it may cause serious consequences such as economic damages and public panic. The misinformation prevention problem aims at generating a positive cascade with appropriate seed nodes in order to compete against the misinformation. In this paper, we study the misinformation prevention problem under the prominent independent cascade model. Due to the #P-hardness in computing influence, the core problem is to design effective sampling methods to estimate the function value. The main contribution of this paper is a novel sampling method. Different from the classic reverse sampling technique which treats all nodes equally and samples the node uniformly, the proposed method proceeds with a hybrid sampling process which is able to attach high weights to the users who are prone to be affected by the misinformation. Consequently, the new sampling method is more powerful in generating effective samples used for computing seed nodes for the positive cascade. Based on the new hybrid sample technique, we design an algorithm offering a (1−1/e− )approximation. We experimentally evaluate the proposed method on extensive datasets and show that it significantly outperforms the state-of-the-art solutions.
Besides, in order to improve time performance better, Tong et al. proposed a novel sampling method, hybrid sampling technique REF , which attached high weights to the users who are prone to be affected by rumor instead of sampling the nodes uniformly.
Beyond Uniform Reverse Sampling: A Hybrid Sampling Technique for Misinformation Prevention
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
Computer Science