2017 IEEE 33rd International Conference on Data Engineering (ICDE) 2017
DOI: 10.1109/icde.2017.134
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Temporal Influence Blocking: Minimizing the Effect of Misinformation in Social Networks

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Cited by 43 publications
(19 citation statements)
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“…For the same purpose, in [23] the authors studied to prevent influence of misinformation on the linear competition model. In addition, the authors in [6] studied the TIB (Temporal Influence Blocking) problem to limit misinformation by time delay. The authors in [24] studied the new β I T problem with a goal of selecting the smallest seed set to start spreading good information to eliminate bad information.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…For the same purpose, in [23] the authors studied to prevent influence of misinformation on the linear competition model. In addition, the authors in [6] studied the TIB (Temporal Influence Blocking) problem to limit misinformation by time delay. The authors in [24] studied the new β I T problem with a goal of selecting the smallest seed set to start spreading good information to eliminate bad information.…”
Section: Related Workmentioning
confidence: 99%
“…There have been previous studies for minimizing the impacts of misinformation diffusion in OSN [3]- [6]. A commonly used method in these studies is to disable users and connections that are considered to have major roles in spreading misinformation [7].…”
Section: Introductionmentioning
confidence: 99%
“…The user and relationships in each training set are shown in Table 1. We measure the effectiveness of each method by Salvation Ratio (SR) [1], [20], [26]. It gives the protected nodes proportion by setting truth node, which denoted as:…”
Section: B Node Update and Node Selectionmentioning
confidence: 99%
“…Lv et al [42] devise a heuristic algorithm based on the community structure of the network for the IBM problem. Song et al [7] consider IBM problem with time delays. Zhu et al [8] propose two heuristic algorithms LIBM-H and LIBM-C to solve the location-aware IBM problem.…”
Section: Influence Blocking Maximizationmentioning
confidence: 99%