Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs. An innovative node relative entropy, which considers node features and structural similarity, is used to measure mutual information between node pairs. In addition, to avoid the sub-optimal solutions caused by mixing useful information and noises of remote nodes, a deep reinforcement learning-based algorithm is developed to optimize the graph topology. This algorithm selects informative nodes and discards noisy nodes based on the defined node relative entropy. Extensive experiments are conducted on seven real-world datasets. The experimental results demonstrate the superiority of GraphRARE in node classification and its capability to optimize the original graph topology.

翻译：图神经网络（GNN）在图分析任务中展现了显著优势。然而，现有方法大多基于同质性假设，在处理异配图时性能较差——在异配图中，相连节点通常具有不同的特征和类别标签，语义相关的节点可能相隔多跳。为解决这一局限，本文提出GraphRARE，一种基于节点相对熵和深度强化学习的通用框架，用于增强GNN的表达能力。我们创新性地定义了节点相对熵，通过融合节点特征与结构相似性来度量节点对之间的互信息。此外，为避免远程节点的有用信息与噪声混合导致的次优解，我们开发了基于深度强化学习的图拓扑优化算法。该算法根据定义的节点相对熵选择信息丰富节点并丢弃噪声节点。在七个真实数据集上的实验表明，GraphRARE在节点分类任务中具有优越性，并能有效优化原始图拓扑。