Revisiting Random Walks for Learning on Graphs

Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade, Youngmin Ryou, Seunghoon Hong

Keywords: Graph machine learning, random walk, invariance, universal approximation, markov chain

TL;DR: From the perspectives of invariance and universality, we revisit a simple idea where a random walk on a graph produces a machine-readable record which is processed by a deep neural network to directly make vertex-level or graph-level predictions.

Abstract: We revisit a recent model class for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural network to directly make vertex-level or graph-level predictions. We refer to these stochastic machines as random walk neural networks (RWNNs), and through principled analysis, show that we can design them to be isomorphism invariant while capable of universal approximation of graph functions in probability. A useful finding is that almost any kind of record of random walk guarantees probabilistic invariance as long as the vertices are anonymized. This enables us, for example, to record random walks in plain text and adopt a language model to read these text records to solve graph tasks. We further establish a parallelism to message passing neural networks using tools from Markov chain theory, and show that over-smoothing in message passing is alleviated by construction in RWNNs, while over-squashing manifests as probabilistic under-reaching. We empirically demonstrate RWNNs on a range of problems, verifying our theoretical analysis and demonstrating the use of language models for separating strongly regular graphs where the 3-WL test fails, and transductive classification on arXiv citation network.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 8462