@inproceedings{SisLab2790,
       booktitle = {10th Asian Conference on Intelligent Information and Database Systems (AIIDS)},
           month = {March},
           title = {Targeted Misinformation Blocking on Online Social Networks},
          author = {Canh V. Pham and Quat V. Phu and Xuan Huan Hoang},
            year = {2018},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2790/},
        abstract = {In this paper, we investigate a problem of ?nding smallest set of nodes to remove from a social network so that in?uence reduction of misinformation sources at least given threshold {\ensuremath{\gamma}}, called Targeted Misinformation Blocking (TMB) problem. We prove that TBM is \#P-hard under LT model. For any parameter ? ? (0,{\ensuremath{\gamma}}), we designed Greedy algorithm which return the solution A with the expected in?uence reduction greater than {\ensuremath{\gamma}}??, and the size of A is within factor 1+ln({\ensuremath{\gamma}}/?) of the optimal size. To speed-up Greedy algorithm, we designed an e?cient heuristic algorithm, called STBM algorithm. Experiments were conducted on real world networks which showed the e?ectiveness of proposed algorithms in term of both e?ectiveness and e?ciency.}
}