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

RetGK: Graph Kernels based on Return Probabilities of Random Walks