The All-Paths and Cycles Graph Kernel

TL;DR

This paper introduces an efficient algorithm for computing the all-paths and cycles graph kernel, including simple cycles, enabling better similarity measurement between graphs for machine learning tasks.

Contribution

It presents a novel, efficient algorithm for the all-paths kernel that also incorporates simple cycles, improving graph similarity measures in kernel methods.

Findings

The all-paths and cycles kernel outperforms the shortest-path kernel in experiments.

The algorithm is feasible for large datasets and moderate path lengths.

The kernel achieves state-of-the-art performance on various graph datasets.

Abstract

With the recent rise in the amount of structured data available, there has been considerable interest in methods for machine learning with graphs. Many of these approaches have been kernel methods, which focus on measuring the similarity between graphs. These generally involving measuring the similarity of structural elements such as walks or paths. Borgwardt and Kriegel proposed the all-paths kernel but emphasized that it is NP-hard to compute and infeasible in practice, favouring instead the shortest-path kernel. In this paper, we introduce a new algorithm for computing the all-paths kernel which is very efficient and enrich it further by including the simple cycles as well. We demonstrate how it is feasible even on large datasets to compute all the paths and simple cycles up to a moderate length. We show how to count labelled paths/simple cycles between vertices of a graph and evaluate a labelled path and simple cycles kernel. Extensive evaluations on a variety of graph datasets demonstrate that the all-paths and cycles kernel has superior performance to the shortest-path kernel and state-of-the-art performance overall.