Deep reinforcement learning-based approach to tackle positive influence
maximization in signed social networks
- Nuan Song,
- Liming Wu,
- Tianqi Sun,
- Wei Si,
- Dong Li
Nuan Song
Shandong University School of Mechanical Electrical and Information Engineering
Author ProfileLiming Wu
Shandong University School of Mechanical Electrical and Information Engineering
Author ProfileTianqi Sun
Shandong University School of Mechanical Electrical and Information Engineering
Author ProfileWei Si
Shandong University School of Mechanical Electrical and Information Engineering
Author ProfileAbstract
The Influence Maximization problem has garnered significant research
interest since its introduction. In 2014, the problem was further
extended to include signed social networks, resulting in the positive
influence maximization problem and negative influence maximization
problem. However, current solutions mainly rely on greedy algorithms
that suffer from the Inefficient shortcoming, which do not leverage deep
reinforcement learning. This paper introduces a novel approach to
maximize the number of positively activated nodes by applying deep
reinforcement learning to signed networks. Specifically, we extend SDGNN
model for network representation learning and design a DQN-based seed
node selection algorithm. The extensive experimental results on two
real-world networks demonstrate our proposed model outperforms greedy
algorithm and CELF algorithm,in terms of both time efficiency and
influence spread quality. To our knowledge,this work is the first to
leverage deep reinforcement learning to solve influence maximization
problem in signed social networks.