Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
| Publisher | International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) |
| Pages | 1423-1431 |
| Number of pages | 9 |
| ISBN (Print) | 9781510868083 |
| State | Published - Jan 1 2018 |
| Event | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden Duration: Jul 10 2018 → Jul 15 2018 |
Publication series
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| Volume | 2 |
| ISSN (Print) | 1548-8403 |
| ISSN (Electronic) | 1558-2914 |
Other
| Other | 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 |
|---|---|
| Country | Sweden |
| City | Stockholm |
| Period | 7/10/18 → 7/15/18 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Software
- Control and Systems Engineering
Cite this
}
Please be an influencer? Contingency-aware influence maximization. / Yadav, Amulya; Onasch-Vera, Laura; Noothigattu, Ritesh; Marcolino, Leandro Soriano; Rice, Eric; Tambe, Milind.
17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018. p. 1423-1431 (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
PY - 2018/1/1
Y1 - 2018/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85054702605&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054702605&partnerID=8YFLogxK
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)
ER -