TY - GEN
T1 - Please be an influencer? Contingency-aware influence maximization
AU - Yadav, Amulya
AU - Onasch-Vera, Laura
AU - Noothigattu, Ritesh
AU - Marcolino, Leandro Soriano
AU - Rice, Eric
AU - Tambe, Milind
N1 - Publisher Copyright:
© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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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M3 - Conference contribution
AN - SCOPUS:85054702605
SN - 9781510868083
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1423
EP - 1431
BT - 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Y2 - 10 July 2018 through 15 July 2018
ER -