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Poster
Robust Influence Maximization for Hyperparametric Models
Dimitrios Kalimeris · Gal Kaplun · Yaron Singer

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #268
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In this paper we study the problem of robust influence maximization in the independent cascade model under a hyperparametric assumption. In social networks users influence and are influenced by individuals with similar characteristics and as such they are associated with some features. A recent surging research direction in influence maximization focuses on the case where the edge probabilities on the graph are not arbitrary but are generated as a function of the features of the users and a global hyperparameter. We propose a model where the objective is to maximize the worst-case number of influenced users for any possible value of that hyperparameter. We provide theoretical results showing that proper robust solution in our model is NP-hard and an algorithm that achieves improper robust optimization. We make-use of sampling based techniques and of the renowned multiplicative weight updates algorithm. Additionally we validate our method empirically and prove that it outperforms the state-of-the-art robust influence maximization techniques.

Keywords: Computational Social Sciences · Networks and Relational Learning · Non-convex Optimization · Robust Statistics and Machine Learning

Author Information

Dimitrios Kalimeris (Harvard University)
Gal Kaplun (Harvard)
Yaron Singer (Harvard)

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