Abstract
In this paper, we investigate a problem of finding smallest set of nodes to remove from a social network so that influence reduction of misinformation sources at least a given threshold \(\gamma \), called Targeted Misinformation Blocking (\(\mathsf {TMB}\)) problem. We prove that \(\mathsf {TBM}\) is #P-hard under LT model. For any parameter \(\epsilon \in (0, \gamma )\), we designed \(\mathsf {Greedy}\) algorithm which return the solution A with the expected influence reduction greater than \(\gamma -\epsilon \), and the size of A is within factor \(1\,+\,\ln (\gamma / \epsilon )\) of the optimal size. To speed-up \(\mathsf {Greedy}\) algorithm, we designed an efficient heuristic algorithm, called \(\mathsf {STBM}\) algorithm. Experiments were conducted on real-world networks which showed the effectiveness of proposed algorithms in term of both effectiveness and efficiency.
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Pham, C.V., Phu, Q.V., Hoang, H.X. (2018). Targeted Misinformation Blocking on Online Social Networks. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10751. Springer, Cham. https://doi.org/10.1007/978-3-319-75417-8_10
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