Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the broad application of GNNs in large-scale industrial settings since the node interaction among rapidly expanding neighbors incurs high computation and memory costs. Second, the over-smoothing problem restricts the discrimination ability of nodes, i.e., node representations of different classes will converge to indistinguishable after repeated node interactions. In this work, we propose a novel hop interaction paradigm to address these limitations simultaneously. The core idea is to convert the interaction target among nodes to pre-processed multi-hop features inside each node. We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction. Furthermore, we propose a multi-task learning strategy with a self-supervised learning objective to enhance HopGNN. We conduct extensive experiments on 12 benchmark datasets in a wide range of domains, scales, and smoothness of graphs. Experimental results show that our methods achieve superior performance while maintaining high scalability and efficiency. The code is at https://github.com/JC-202/HopGNN.

翻译：现有图神经网络遵循消息传递机制，通过节点间迭代进行信息交互。尽管已取得显著进展，但这类节点交互范式仍存在以下局限：首先，可扩展性限制导致GNN难以大规模工业应用，因为快速扩展的邻居节点交互会带来高昂的计算和内存成本；其次，过平滑问题制约了节点的判别能力——不同类别的节点表征经过重复节点交互后趋于不可区分。本研究提出一种新型跳数交互范式以同时解决上述局限，其核心思想是将节点间的交互目标转化为各节点内部预处理的多跳特征。我们设计了简洁高效的HopGNN框架，可便捷利用现有GNN实现跳数交互，并进一步提出含自监督学习目标的多任务学习策略以增强HopGNN。在涵盖不同领域、规模及图平滑度的12个基准数据集上的实验表明，本方法在保持高可扩展性和高效率的同时，取得了优越性能。代码已开源：https://github.com/JC-202/HopGNN。