论文标题
通过增强学习和图形神经网络控制图动力学
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks
论文作者
论文摘要
我们考虑通过有限数量的干预措施在图表上控制部分观察到的动态过程的问题。这个问题自然出现在诸如调度病毒测试以遏制流行病之类的情况下出现。有针对性的营销以推广产品；并手动检查帖子以检测到在社交网络上传播的虚假新闻。 我们在时间图过程中将此设置作为顺序决策问题提出。面对指数状态空间，组合动作空间和部分可观察性，我们设计了一种可拖动的方案，以控制时间图上的动态过程。我们成功地将方法应用于我们框架中的两个流行问题：确定应测试哪些节点以遏制流行病的传播并影响图表上的最大化。
We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to promote a product; and manually inspecting posts to detect fake news spreading on social networks. We formulate this setup as a sequential decision problem over a temporal graph process. In face of an exponential state space, combinatorial action space and partial observability, we design a novel tractable scheme to control dynamical processes on temporal graphs. We successfully apply our approach to two popular problems that fall into our framework: prioritizing which nodes should be tested in order to curb the spread of an epidemic, and influence maximization on a graph.
