Graph Learning with 1D Convolutions on Random Walks.

arXiv (Cornell University)

Abstract

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 classification and regression on graphs.

Authors 4

1. Jan Toenshoff Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Martin Ritzert Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Hinrikus Wolf Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Martin Grohe Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University