2,710 research outputs found
Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network
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 multiplicative ratio and an
additive error, where 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
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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