1. MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100
Abstract In the existing graph kernels, local attributes of graphs are concerned and local topological features are utilized to compute the similarity measurement of graphs. However, hierarchical structure information of the graph is ignored. To handle this problem, optimal transport based hierarchical graph kernel is proposed. Firstly, each graph is represented as a hierarchical graph structure. During the constructive process of hierarchical graph structure, K-means clustering algorithm is employed to construct new nodes and probabilities of connections between new nodes is regarded as edges of graph at each layer. Then, the optimal transport distance between paired graphs in the special hierarchical structure is calculated using optimal transport with entropic constraints. Finally, the optimal transport distance based hierarchical graph kernel is calculated. The experimental results on six graph datasets show that the classification performance is significantly improved by the proposed graph kernel.
Fund:National Key Research and Development Program of China(No.2018YFC2001600,2018YFC2001602,2018ZX10201002), National Natural Science Foundation of China(No.61876082,61732006,61861130366)
Corresponding Authors:
ZHANG Daoqiang, Ph.D., professor. His research in-terests include machine learning, pattern re-cognition and medical image analysis.
About author:: MA Kai, Ph.D. candidate. His research interests include machine learning and medical image analysis. HUANG Shuo, Ph.D. candidate. His research interests include machine learning and medical image analysis.
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2021, Vol. 34 
