Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, the researchers propose an extension to the independent cascad...

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Bibliographic Details
Published inSociety for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining p. 379
Main Authors Chen, Wei, Collins, Alex, Cummings, Rachel, Ke, Te, Liu, Zhenming, Rincon, David, Sun, Xiaorui, Wang, Yajun, Wei, Wei, Yuan, Yifei
Format Conference Proceeding
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2011
Subjects
Algorithms
Approximation
Heuristic
Influence
Opinions
Simulation
Social networks
Studies
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Summary:Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, the researchers propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of ... They design an efficient algorithm to compute influence in tree structures, which is nontrivial due to the negativity bias in the model. Through simulations, they show that their heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.(ProQuest: ... denotes formulae/symbols omitted.)