NIPS Proceedingsβ

RetGK: Graph Kernels based on Return Probabilities of Random Walks

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018)

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Conference Event Type: Poster


Graph-structured data arise in wide applications, such as computer vision, bioinformatics, and social networks. Quantifying similarities among graphs is a fundamental problem. In this paper, we develop a framework for computing graph kernels, based on return probabilities of random walks. The advantages of our proposed kernels are that they can effectively exploit various node attributes, while being scalable to large datasets. We conduct extensive graph classification experiments to evaluate our graph kernels. The experimental results show that our graph kernels significantly outperform other state-of-the-art approaches in both accuracy and computational efficiency.