Recently, maximizing mutual information has emerged as a powerful tool for unsupervised graph representation learning. Existing methods are typically effective in capturing graph information from the topology view but consistently ignore the node feature view. To circumvent this problem, we propose a novel method by exploiting mutual information maximization across feature and topology views. Specifically, we first construct the feature graph to capture the underlying structure of nodes in feature spaces by measuring the distance between pairs of nodes. Then we use a cross-view representation learning module to capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we develop a common representation learning module by using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, the proposed method achieves comparable or even better performance under the unsupervised representation and linear evaluation protocol.

Since learning action selection strategy is time-consuming due to the reinforcement learning algorithm, a heuristic reinforcement learning algorithm is presented based on the state backtracking reinforcement learning to improve the action selection strategy of the reinforcement learning. The selection strategies of repeated the action are analyzed and compared by state backtracking. A cost function is defined to denote the importance of repetitive actions. A novel heuristic function is given by combing the action-reward with the cost of an action. This algorithm reinforces the important of an action by heuristic function to speed learning and reduces unnecessary explorations by the cost function, so as to steadily improve the learning efficiency. The simulation results of two robot games proves that the algorithm can effectively enhancement the learning rate of Q-learning based on the state backtracking heuristic reinforcement learning method.