  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.
