We propose CRaWl (CNNs for Random Walks), a novel neural network architecture for graph learning. It is based on processing sequences of small subgraphs induced by random walks with standard 1D CNNs. Thus, CRaWl is fundamentally different from typical message passing graph neural network architectures. It is inspired by techniques counting small subgraphs, such as the graphlet kernel and motif counting, and combines them with random walk based techniques in a highly efficient and scalable neural architecture. We demonstrate empirically that CRaWl matches or outperforms state-of-the-art GNN architectures across a multitude of benchmark datasets for graph learning.

中文翻译：

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随机游动中一维卷积的图学习

我们提出CRaWl（随机游走的CNN），这是一种用于图学习的新型神经网络体系结构。它基于处理由标准1D CNN随机游走引起的小子图的序列。因此，CRaW1从根本上不同于典型的消息传递图神经网络体系结构。它的灵感来自于对小子图进行计数的技术，例如小图核和基序计数，并将它们与基于随机游走的技术相结合，构成了高效且可扩展的神经体系结构。我们通过经验证明，CRaWl在用于图学习的众多基准数据集上匹配或优于最新的GNN架构。