TY - GEN
T1 - Graph Influence Maximization Algorithm Based on Reinforcement Learning-PPO Algorithm
AU - Zhang, Wenxin
AU - Ma, Yaofei
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Information dissemination in social network has gained great attention in the study of complex networks. In this paper, we propose a novel network influence maximization algorithm incorporating graph representation learning and reinforcement learning, called RL-PPO, to select a set of seed nodes as the starter nodes and by which the information can be propagated as fast as could in the network,i.e., reaching the status of influence maximization. This algorithm take the feature matrix and adjacency matrix of the network as inputs, and the seed nodes as outputs.To better represent the network, the graph representation learning method is employed to extract network features and to perform feature aggregation. The experiment results show that, compared with the traditional greedy algorithm, the seed nodes determined by this proposed algorithm GraphPPO exhibited higher propagation efficiency.
AB - Information dissemination in social network has gained great attention in the study of complex networks. In this paper, we propose a novel network influence maximization algorithm incorporating graph representation learning and reinforcement learning, called RL-PPO, to select a set of seed nodes as the starter nodes and by which the information can be propagated as fast as could in the network,i.e., reaching the status of influence maximization. This algorithm take the feature matrix and adjacency matrix of the network as inputs, and the seed nodes as outputs.To better represent the network, the graph representation learning method is employed to extract network features and to perform feature aggregation. The experiment results show that, compared with the traditional greedy algorithm, the seed nodes determined by this proposed algorithm GraphPPO exhibited higher propagation efficiency.
KW - Complex networks
KW - Graph representation learning
KW - Influence maximization
KW - Reinforcement learning
UR - https://www.scopus.com/pages/publications/85140484757
U2 - 10.1007/978-981-19-6203-5_55
DO - 10.1007/978-981-19-6203-5_55
M3 - 会议稿件
AN - SCOPUS:85140484757
SN - 9789811962028
T3 - Lecture Notes in Electrical Engineering
SP - 563
EP - 572
BT - Proceedings of 2022 Chinese Intelligent Systems Conference - Volume I
A2 - Jia, Yingmin
A2 - Zhang, Weicun
A2 - Fu, Yongling
A2 - Zhao, Shoujun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th Chinese Intelligent Systems Conference, CISC 2022
Y2 - 15 October 2022 through 16 October 2022
ER -