Deep Wasserstein Graph Discriminant Learning for Graph Classification

Authors

  • Tong Zhang Nanjing University of Science and Technology
  • Yun Wang Nanjing University of Science and Technology
  • Zhen Cui Nanjing University of Science and Technology
  • Chuanwei Zhou Nanjing University of Science and Technology
  • Baoliang Cui Alibaba Group
  • Haikuan Huang Alibaba Group
  • Jian Yang Nanjing University of Science and Technology

Keywords:

Graph-based Machine Learning

Abstract

Graph topological structures are crucial to distinguish different-class graphs. In this work, we propose a deep Wasserstein graph discriminant learning (WGDL) framework to learn discriminative embeddings of graphs in Wasserstein-metric (W-metric) matching space. In order to bypass the calculation of W-metric class centers in discriminant analysis, as well as better support batch process learning, we introduce a reference set of graphs (aka graph dictionary) to express those representative graph samples (aka dictionary keys). On the bridge of graph dictionary, every input graph can be projected into the latent dictionary space through our proposed Wasserstein graph transformation (WGT). In WGT, we formulate inter-graph distance in W-metric space by virtue of the optimal transport (OT) principle, which effectively expresses the correlations of cross-graph structures. To make WGDL better representation ability, we dynamically update graph dictionary during training by maximizing the ratio of inter-class versus intra-class Wasserstein distance. To evaluate our WGDL method, comprehensive experiments are conducted on six graph classification datasets. Experimental results demonstrate the effectiveness of our WGDL, and state-of-the-art performance.

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Published

2021-05-18

How to Cite

Zhang, T., Wang, Y., Cui, Z., Zhou, C., Cui, B., Huang, H., & Yang, J. (2021). Deep Wasserstein Graph Discriminant Learning for Graph Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10914-10922. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17303

Issue

Section

AAAI Technical Track on Machine Learning V