Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs.

翻译：图神经网络（GNNs）近年来越来越受到关注，并在许多基于图的任务中取得了显著性能，尤其是在图的半监督学习方面。然而，现有的大多数GNNs基于消息传递范式，在单一拓扑空间中迭代聚合邻域信息。尽管取得了成功，但GNNs的表达能力受到一些缺陷的限制，例如消息源扩展的不灵活性、对节点级消息输出差异的忽视以及单一消息空间的约束。为解决这些问题，我们提出了一种新的消息传递范式，其基于多步消息源、节点特定消息输出和多空间消息交互的特性。为验证其有效性，我们将该新消息传递范式实例化为双感知图神经网络（DPGNN），该网络采用节点到步的注意力机制自适应地聚合节点特定的多步邻域信息。我们提出的DPGNN能够同时捕获结构邻域信息和特征相关信息，用于图表示学习。在六个具有不同拓扑结构的基准数据集上的实验结果表明，我们的方法优于最新的最先进模型，证明了我们方法的优越性和通用性。据我们所知，我们是第一个在GNNs中考虑节点特定消息传递的工作。