Adaptive Graph Convolutional Neural Networks

Authors

  • Ruoyu Li The University of Texas at Arlington
  • Sheng Wang The University of Texas at Arlington
  • Feiyun Zhu The University of Texas at Arlington
  • Junzhou Huang The University of Texas at Arlington

Keywords:

graph CNN, spectral filter, metric learning

Abstract

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for each graph data while training. To efficiently learn the graph, a distance metric learning is proposed. Extensive experiments on nine graph-structured datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy.

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Published

2018-04-29

How to Cite

Li, R., Wang, S., Zhu, F., & Huang, J. (2018). Adaptive Graph Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11691