  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 existing
state-of-the-art approaches in both accuracy and computational efficiency.
