Please be an Influencer? : Contingency-Aware Influence Maximization

Yadav, Amulya and Noothigattu, Ritesh and Rice, Eric and Onasch-Vera, Laura and Soriano Marcolino, Leandro and Tambe, Milind (2018) Please be an Influencer? : Contingency-Aware Influence Maximization. In: AAMAS '18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems :. International Foundation for Autonomous Agents and Multiagent Systems, SWE, pp. 1423-1421. ISBN 9781450356497

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Most previous work on influence maximization in social networks assumes that the chosen influencers (or seed nodes) can be influenced with certainty (i.e., with no contingencies). In this paper, we focus on using influence maximization in public health domains for assisting low-resource communities, where contingencies are common. It is very difficult in these domains to ensure that the seed nodes are influenced, as influencing them entails contacting/convincing them to attend training sessions, which may not always be possible. Unfortunately, previous state-of-the-art algorithms for influence maximization are unusable in this setting. This paper tackles this challenge via the following four contributions: (i) we propose the Contingency Aware Influence Maximization problem and analyze it theoretically; (ii) we cast this problem as a Partially Observable Markov Decision Process and propose CAIMS (a novel POMDP planner) to solve it, which leverages a natural action space factorization associated with real-world social networks; and (iii) we provide extensive simulation results to compare CAIMS with existing state-of-the-art influence maximization algorithms. Finally, (iv) we provide results from a real-world feasibility trial conducted to evaluate CAIMS, in which key influencers in homeless youth social networks were influenced in order to spread awareness about HIV.

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22 Feb 2018 14:36
Last Modified:
10 Jan 2024 00:43