The wide adoption of social media platforms has brought about numerous benefits for communication and information sharing. However, it has also led to the rapid spread of misinformation, causing significant harm to individuals, communities, and society at large. Consequently, there has been a growing interest in devising efficient and effective strategies to contain the spread of misinformation. One popular countermeasure is blocking edges in the underlying network. We model the spread of misinformation using the classical Independent Cascade model and study the problem of minimizing the spread by blocking a given number of edges. We prove that this problem is computationally hard, but we propose an intuitive community-based algorithm, which aims to detect well-connected communities in the network and disconnect the inter-community edges. Our experiments on various real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures.

翻译：社交媒体的广泛应用为信息沟通与分享带来了诸多便利。然而，这也导致了虚假信息的快速传播，对个人、社区乃至整个社会造成重大危害。因此，设计高效且有效的策略来遏制虚假信息传播日益受到关注。一种流行的应对措施是阻断底层网络中的边。我们采用经典的独立级联模型对虚假信息传播进行建模，并研究通过阻断给定数量的边来最小化传播范围的问题。我们证明了该问题的计算复杂性，但提出了一种基于社区的直观算法，旨在检测网络中紧密连接的社区并断开社区间的边。在多种真实社交网络上的实验表明，所提算法显著优于主要依赖中心性度量的现有方法。