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Entropy 2018, 20(12), 984; https://doi.org/10.3390/e20120984

A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs

1
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China
2
National Innovation Institute of Defense Technology, Academy of Military Science, Beijing 100071, China
*
Author to whom correspondence should be addressed.
Received: 25 September 2018 / Revised: 16 December 2018 / Accepted: 16 December 2018 / Published: 18 December 2018
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Abstract

Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of real-world and synthetic datasets, kernels are compared by many criteria such as classification accuracy, F1 score, runtime cost, scalability and applicability. Finally, quantitative conclusions are discussed based on the analyses of the extensive experimental results. The main contribution of this paper is that a comprehensive evaluation framework of graph kernels is proposed, which is significant for graph-classification applications and the future kernel research. View Full-Text
Keywords: graph kernel; unattributed graph; time complexity; classification accuracy; graph dataset graph kernel; unattributed graph; time complexity; classification accuracy; graph dataset
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Zhang, Y.; Wang, L.; Wang, L. A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs. Entropy 2018, 20, 984.

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