Extending local features with contextual information in graph kernels

TL;DR

This paper introduces a novel graph kernel that incorporates contextual information into local features, improving the discrimination of substructures by considering their surrounding context, with efficient computation and promising classification results.

Contribution

It proposes a new graph kernel that associates context with substructure features, enhancing the expressiveness of graph comparison methods.

Findings

Efficient algorithm for kernel computation

Improved classification performance on real datasets

Context-aware substructure matching enhances discrimination

Abstract

Graph kernels are usually defined in terms of simpler kernels over local substructures of the original graphs. Different kernels consider different types of substructures. However, in some cases they have similar predictive performances, probably because the substructures can be interpreted as approximations of the subgraphs they induce. In this paper, we propose to associate to each feature a piece of information about the context in which the feature appears in the graph. A substructure appearing in two different graphs will match only if it appears with the same context in both graphs. We propose a kernel based on this idea that considers trees as substructures, and where the contexts are features too. The kernel is inspired from the framework in [6], even if it is not part of it. We give an efficient algorithm for computing the kernel and show promising results on real-world graph classification datasets.