Paper Info
Title
Belief and Opinion Evolution in Social Networks: A High-Dimensional Mean Field Game Approach
Abstract
Belief and opinion evolution in social networks (SNs) can aid in understanding how people influence others' decisions through social relationships as well as provide a solid foundation for many valuable social applications. As large numbers of users are involved in SNs, the complexity of traditional optimization techniques is high as they deal with the interactions between users separately. Moreover, the state variable (opinion) is high-dimensional because a person usually has opinions about many different social issues. To overcome those challenges, we formulate the opinion evolution in SNs as a high-dimensional stochastic mean field game (MFG). Numerical methods for high-dimensional MFGs are practically non-existent because of the need for grid-based spatial discretization. Thus, we propose a machine-learning based method, where we use an alternating population and agent control neural network (APAC-net), to tractably solve high-dimensional stochastic MFGs. Through APAC-net, solving MFGs can be regarded as a special case of training a generative adversarial network (GAN). To the best of our knowledge, the APAC-Net is the first model that can solve high-dimensional stochastic MFGs. The simulation results affirm the efficiency of the APAC-net.
Year
DOI
Venue
2021
10.1109/ICC42927.2021.9500884
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)
DocType
ISSN
Citations 
Conference
1550-3607
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hao Gao18411.52
Alex Tong Lin201.01
Reginald A. Banez301.35
Wuchen Li400.34
Zhu Han511215760.71
Stanley J. Osher621.72
H. V. Poor7254111951.66