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Updated: Feb 15, 2026

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通过连续时间图形神经网络和深度强化学习来最大化时间影响

Yong Wang1, Mohamad A Alawad2, Raed H C Alfilh3

  • 1School of Information Engineering, Yulin University, Yulin, 719000, Shaanxi, China.

Scientific reports
|February 13, 2026
PubMed
概括

TempRL-IM是一种时间增强学习框架,通过使用连续时间图神经网络 (CTGNNs) 和双深Q网络 (DDQN) 代理来增强动态网络中的影响力最大化. 这种方法可以提高信息传播和推断速度.

关键词:
连续时间建模.深度强化学习的学习.动态网络 动态网络是一个动态网络.影响力最大化影响力最大化小型企业 小型企业时间图神经网络的神经网络

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