TY - GEN
T1 - Bridging the gap between theory and practice in influence maximization
T2 - 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
AU - Yadav, Amulya
AU - Wilder, Bryan
AU - Rice, Eric
AU - Petering, Robin
AU - Craddock, Jaih
AU - Yoshioka-Maxwell, Amanda
AU - Hemler, Mary
AU - Onasch-Vera, Laura
AU - Tambe, Milind
AU - Woo, Darlene
N1 - Funding Information:
This research was supported by MURI grant W911NF-11-1-0332 and NIMH Grant R01-MH093336.
Publisher Copyright:
© 2018 International Joint Conferences on Artificial Intelligence.All right reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key”seed” nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.
AB - This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key”seed” nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ~160% in terms of information spread about HIV among homeless youth.
UR - http://www.scopus.com/inward/record.url?scp=85055700974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055700974&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2018/761
DO - 10.24963/ijcai.2018/761
M3 - Conference contribution
AN - SCOPUS:85055700974
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 5399
EP - 5403
BT - Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
A2 - Lang, Jerome
PB - International Joint Conferences on Artificial Intelligence
Y2 - 13 July 2018 through 19 July 2018
ER -