2,710 research outputs found

    Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network

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    A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum size so that with a guaranteed probability the number of users discussing the topic would reach a given threshold. We formulate the task as an optimization problem called seed minimization with probabilistic coverage guarantee (SM-PCG). This problem departs from the previous studies on social influence maximization or seed minimization because it considers influence coverage with probabilistic guarantees instead of guarantees on expected influence coverage. We show that the problem is not submodular, and thus is harder than previously studied problems based on submodular function optimization. We provide an approximation algorithm and show that it approximates the optimal solution with both a multiplicative ratio and an additive error. The multiplicative ratio is tight while the additive error would be small if influence coverage distributions of certain seed sets are well concentrated. For one-way bipartite graphs we analytically prove the concentration condition and obtain an approximation algorithm with an O(logn)O(\log n) multiplicative ratio and an O(n)O(\sqrt{n}) additive error, where nn is the total number of nodes in the social graph. Moreover, we empirically verify the concentration condition in real-world networks and experimentally demonstrate the effectiveness of our proposed algorithm comparing to commonly adopted benchmark algorithms.Comment: Conference version will appear in KDD 201

    Blocking Negative Influential Node Set in Social Networks: From Host Perspective

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    Nowadays, social networks are considered as the very important medium for the spreading of information, innovations, ideas and influences among individuals. Viral marketing is a most prominent marketing strategy using word-of-mouth advertising in social networks. The key problem with the viral marketing is to find the set of influential users or seeds, who, when convinced to adopt an innovation or idea, shall influence other users in the network, leading to large number of adoptions. In our study, we propose and study the competitive viral marketing problem from the host perspective, where the host of the social network sells the viral marketing campaigns to its customers and keeps control of the allocation of seeds. Seeds are allocated in such a way that it creates the bang for the buck for each company. We propose a new diffusion model considering both negative and positive influences. Moreover, we propose a novel problem, named Blocking Negative Influential Node Set (BNINS) selection problem, to identify the positive node set such that the number of negatively activated nodes is minimized for all competitors. Then we proposed a solution to the BNINS problem and conducted simulations to validate the proposed solution. We also compare our work with the related work to check the performance of BNINS-GREEDY under different metrics and we observed that BNINS-GREEDY outperforms the others\u27 algorithm. For Random Graph, on average, BNINS-GREEDY blocks the negative influence 17.22% more than CLDAG. At the same time, it achieves 7.6% more positive influence propagation than CLDAG
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