Wu Hong, Zhang Zhijian, Fang Yabo, Zhang Shaotang, Jiang Zuo, Huang Jian, Li Ping
School of Information Engineering, Qujing Normal University, Qujing 655000, China.
Faculty of Science, Kunming University of Science and Technology, Kunming 650031, China.
Math Biosci Eng. 2021 Mar 18;18(3):2614-2631. doi: 10.3934/mbe.2021133.
With the popularity of online social network these have become important platforms for the spread of information. This not only includes correct and useful information, but also false information, and even rumors which could result in panic. Therefore, the containment of rumor spread in social networks is important. In this paper, we propose an effective method that involves selecting a set of nodes in (k, η)-cores and immunize these nodes for rumor containment. First, we study rumor influence propagation in social networks under the extended Independent Cascade (EIC) model, an extension of the classic Independent Cascade (IC) model. Then, we decompose a social network into subgraphs via core decomposition of uncertain graphs and compute the number of immune nodes in each subgraph. Further we greedily select nodes with the Maximum Marginal Covering Neighbors Set as immune nodes. Finally, we conduct experiments using real-world datasets to evaluate our method. Experimental results show the effectiveness of our method.
随着在线社交网络的普及，这些网络已成为信息传播的重要平台。这不仅包括正确且有用的信息，还包括虚假信息，甚至可能导致恐慌的谣言。因此，遏制社交网络中的谣言传播至关重要。在本文中，我们提出了一种有效的方法，该方法涉及在(k, η)-核中选择一组节点并对这些节点进行免疫以遏制谣言。首先，我们在扩展独立级联（EIC）模型（经典独立级联（IC）模型的扩展）下研究社交网络中的谣言影响传播。然后，我们通过不确定图的核心分解将社交网络分解为子图，并计算每个子图中的免疫节点数量。进一步地，我们贪婪地选择具有最大边际覆盖邻居集的节点作为免疫节点。最后，我们使用真实世界数据集进行实验以评估我们的方法。实验结果表明了我们方法的有效性。
