A Reinforcement Learning Approach for Graph Rule Learning

We study the problem of learning rules for graphs. Traditional methods often suffer from large search spaces due to the enumeration of all candidate rules. Although some recent neural logic methods are more efficient in learning rules, they are generally restricted to learning chain-like rules with limited expressiveness. Taking the advantage of Reinforcement Learning (RL) in reducing search space, we implement a policy network based RL method for learning graph rules, denoted as GraphRulRL. In our research, we convert graph rules into sequences of edges, transforming the task of graph rule learning into a process of sequentially adding edges that can be solved by RL. Specifically, GraphRulRL follows a two-stage framework. In the first stage, we train a policy network for graph rule learning, which evaluates graph rules using support with anti-monotonicity as rewards during training. In the second stage, we integrate the well-trained policy network with beam search for iterative searching to generate graph rules. Experimental results prove the effectiveness of the proposed method.