Poster
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang · Mianzhi Wang · Yijian Xiang · Yan Huang · Arye Nehorai

Thu Dec 6th 05:00 -- 07:00 PM @ Room 210 #83

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.

Author Information

Zhen Zhang (WASHINGTON UNIVERSITY IN ST.LOUIS)
Mianzhi Wang (Washington University in St. Louis)
Yijian Xiang (Washington University in St. Louis)
Yan Huang (Washington University in St. Louis)
Arye Nehorai (WASHINGTON UNIVERSITY IN ST.LOUIS)

More from the Same Authors