Adaptive Graph Convolutional Neural Networks
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.
Cite
@article{arxiv.1801.03226,
title = {Adaptive Graph Convolutional Neural Networks},
author = {Ruoyu Li and Sheng Wang and Feiyun Zhu and Junzhou Huang},
journal= {arXiv preprint arXiv:1801.03226},
year = {2018}
}
Comments
The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 8 pages