Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in various graph representation learning tasks. However, most existing GNNs focus primarily on capturing local information through explicit graph convolution, often neglecting global message-passing. This limitation hinders the establishment of a collaborative interaction between global and local information, which is crucial for comprehensively understanding graph data. To address these challenges, we propose a novel framework called Comprehensive Graph Representation Learning (ComGRL). ComGRL integrates local information into global information to derive powerful representations. It achieves this by implicitly smoothing local information through flexible graph contrastive learning, ensuring reliable representations for subsequent global exploration. Then ComGRL transfers the locally derived representations to a multi-head self-attention module, enhancing their discriminative ability by uncovering diverse and rich global correlations. To further optimize local information dynamically under the self-supervision of pseudo-labels, ComGRL employs a triple sampling strategy to construct mixed node pairs and applies reliable Mixup augmentation across attributes and structure for local contrastive learning. This approach broadens the receptive field and facilitates coordination between local and global representation learning, enabling them to reinforce each other. Experimental results across six widely used graph datasets demonstrate that ComGRL achieves excellent performance in node classification tasks. The code could be available at https://github.com/JinluWang1002/ComGRL.

翻译：图神经网络（GNNs）在各种图表示学习任务中展现出卓越的有效性。然而，现有大多数GNN主要通过显式图卷积捕获局部信息，往往忽视全局消息传递。这一局限阻碍了全局与局部信息之间协同交互的建立，而这对全面理解图数据至关重要。为应对这些挑战，我们提出了一种名为全面图表示学习（ComGRL）的新框架。ComGRL将局部信息整合到全局信息中，以推导出强大的表示。它通过灵活的图对比学习隐式平滑局部信息，确保为后续全局探索提供可靠表示。随后，ComGRL将局部导出的表示传递至多头自注意力模块，通过发掘多样且丰富的全局相关性来增强其判别能力。为进一步在伪标签自监督下动态优化局部信息，ComGRL采用三重采样策略构建混合节点对，并在属性和结构上应用可靠的Mixup增强进行局部对比学习。该方法拓宽了感受野，促进了局部与全局表示学习之间的协调，使其能够相互增强。在六个广泛使用的图数据集上的实验结果表明，ComGRL在节点分类任务中实现了优异的性能。代码可在https://github.com/JinluWang1002/ComGRL获取。