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Convolutional Kernel Networks for Graph-Structured Data

Dexiong Chen 1 Laurent Jacob 2 Julien Mairal 1
1 Thoth - Apprentissage de modèles à partir de données massives
LJK - Laboratoire Jean Kuntzmann , Inria Grenoble - Rhône-Alpes
2 Statistique en grande dimension pour la génomique
PEGASE - Département PEGASE [LBBE]
Abstract : We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps, where each node carries information about local graph substructures. On the one hand, the kernel point of view offers an unsupervised, expressive, and easy-to-regularize data representation, which is useful when limited samples are available. On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks. We show that our method achieves competitive performance on several graph classification benchmarks, while offering simple model interpretation. Our code is freely available at https://github.com/claying/GCKN.
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https://hal.archives-ouvertes.fr/hal-02504965
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Submitted on : Wednesday, March 11, 2020 - 10:36:56 AM
Last modification on : Tuesday, July 20, 2021 - 5:20:05 PM
Long-term archiving on: : Friday, June 12, 2020 - 2:30:40 PM

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Dexiong Chen, Laurent Jacob, Julien Mairal. Convolutional Kernel Networks for Graph-Structured Data. 2020. ⟨hal-02504965v1⟩

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