Extending local features with contextual information in graph kernels
This work addresses the challenge of enhancing predictive accuracy in graph classification for machine learning applications, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of improving graph kernels by incorporating contextual information for substructures, resulting in a new kernel that shows promising results on real-world graph classification datasets.
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.