AKBR: Learning Adaptive Kernel-based Representations for Graph Classification

In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aims to define an end-to-end representation learning model to construct an adaptive kernel matrix for graphs. To this end, we commence by leveraging a novel feature-channel attention mechanism to capture the interdependencies between different substructure invariants of original graphs. The proposed AKBR model can thus effectively identify the structural importance of different substructures, and compute the R-convolution kernel between pairwise graphs associated with the more significant substructures specified by their structural attentions. Furthermore, the proposed AKBR model employs all sample graphs as the prototype graphs, naturally providing an end-to-end learning architecture between the kernel computation as well as the classifier. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks.

Authors

• Lu Bai School of Artificial Intelligence, and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing Normal University
• Feifei Qian School of Artificial Intelligence, and Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education, Beijing Normal University, Beijing, China
• Lixin Cui School of Information, Central University of Finance and Economics, Beijing, China
• Ming Li
• Hangyuan Du School of Computer and Information Technology, Shanxi University, Taiyuan, China
• Yue Wang
• Edwin Hancock Department of Computer Science, University of York, York, United Kingdom

Keywords

• Data Mining: DM: Mining graphs
• Machine Learning: ML: Classification
• Machine Learning: ML: Deep learning architectures
• Machine Learning: ML: Kernel methods