TY - GEN

T1 - Please be an Influencer?

T2 - 17th International Conference on Autonomous Agents and Multi-agent Systems

AU - Yadav, Amulya

AU - Noothigattu, Ritesh

AU - Rice, Eric

AU - Onasch-Vera, Laura

AU - Soriano Marcolino, Leandro

AU - Tambe, Milind

N1 - Conference code: 17

PY - 2018/7/10

Y1 - 2018/7/10

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/convincingthem 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 trialconducted 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/convincingthem 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 trialconducted to evaluate CAIMS, in which key influencers in homeless youth social networks were influenced in order to spread awareness about HIV.

M3 - Conference contribution/Paper

SN - 9781450356497

SP - 1423

EP - 1421

BT - AAMAS '18 Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems

A2 - Dastani, M.

A2 - Sukthankar, G.

A2 - Andre, E.

A2 - Koenig, S.

PB - International Foundation for Autonomous Agents and Multiagent Systems

CY - Richland SC

Y2 - 10 July 2018 through 15 July 2018

ER -