Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

2022 ◽  
pp. 1-15
Author(s):  
Lijia Ma ◽  
Zengyang Shao ◽  
Xiaocong Li ◽  
Qiuzhen Lin ◽  
Jianqiang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Influence Maximization

scholarly journals Influence maximization in complex networks through optimal percolation

Nature ◽  
2015 ◽  
Vol 524 (7563) ◽  
pp. 65-68 ◽  
Author(s):  
Flaviano Morone ◽  
Hernán A. Makse
Keyword(s):  
Complex Networks ◽  
Influence Maximization

scholarly journals Finding key players in complex networks through deep reinforcement learning

2020 ◽  
Vol 2 (6) ◽  
pp. 317-324 ◽  
Author(s):  
Changjun Fan ◽  
Li Zeng ◽  
Yizhou Sun ◽  
Yang-Yu Liu
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Key Players

scholarly journals Modeling Complex Networks Based on Deep Reinforcement Learning

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenbo Song ◽  
Wei Sheng ◽  
Dong Li ◽  
Chong Wu ◽  
Jun Ma
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Network Topology ◽  
Network Structure ◽  
Dynamic Change ◽  
Intelligent Agent ◽  
Small World ◽  
Network Nodes ◽  
Scale Free ◽  
Internal Mechanism

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

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