论文标题
通过Edge-Awane Weisfeiler-Leman算法赋予GNNS
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
论文作者
论文摘要
已知消息传递图神经网络（GNN）具有1维Weisfeiler-Leman（1-WL）算法的表现力上限。为了实现更强大的GNN，现有的尝试要么需要临时功能，要么涉及产生高时空和空间复杂性的操作。在这项工作中，我们提出了一个一般且可证明的强大的GNN框架，以保留消息传递方案的可扩展性。特别是，我们首先建议通过考虑邻居之间的边缘来增强图形同构测试的能力，从而产生NC-1-WL。 NC-1-WL的表现力在理论上严格高于1-WL，低于3-WL。此外，我们将NC-GNN框架作为NC-1-WL的神经版本。我们对NC-GNN的简单实现与NC-1-WL一样强大。实验表明，我们的NC-GNN对各种基准有效有效地性能。
Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) algorithm. To achieve more powerful GNNs, existing attempts either require ad hoc features, or involve operations that incur high time and space complexities. In this work, we propose a general and provably powerful GNN framework that preserves the scalability of the message passing scheme. In particular, we first propose to empower 1-WL for graph isomorphism test by considering edges among neighbors, giving rise to NC-1-WL. The expressiveness of NC-1-WL is shown to be strictly above 1-WL and below 3-WL theoretically. Further, we propose the NC-GNN framework as a differentiable neural version of NC-1-WL. Our simple implementation of NC-GNN is provably as powerful as NC-1-WL. Experiments demonstrate that our NC-GNN performs effectively and efficiently on various benchmarks.
