In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub.¹

近年来，基于引文图的任务（例如专利分析、社交网络分析和推荐系统）的商业需求大幅增长。图神经网络（GNN）因其在捕获拓扑图信息方面的卓越性能而被广泛用于这些任务。然而，GNN 的输出结果高度依赖于拓扑结构内局部邻居的组成。为了解决这个问题，我们在引文图中识别了两种类型的邻居：基于拓扑结构的显式邻居和基于节点特征的隐式邻居。我们的主要动机是清晰地定义和可视化这些邻居，强调它们在增强图神经网络性能方面的重要性。我们提出了一个相关感知网络（CNet）来重新组织引文图，并通过利用这些隐式和显式邻居来学习更有价值的信息表示。我们的方法旨在提高图数据增强和分类性能，我们的大部分重点是说明使用这些邻居的重要性，同时还引入了一种新的图数据增强方法。我们将 CNet 与最先进的 (SOTA) GNN 以及作用于 GNN 的其他图数据增强方法进行比较。大量实验表明，CNet 有效地从引文图中提取了更有价值的信息表示，显着优于基线。该代码可在公共 GitHub 上获取。¹