Mitigating Misinformation Spreading in Social Networks via Edge Blocking

We study the problem of mitigating the spread of misinformation in social networks, simulated by the Independent Cascade model. 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 real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures.