Large-Scale Learnable Graph Convolutional Networks

12 Aug 2018Hongyang Gao • Zhengyang Wang • Shuiwang Ji

However, the number of neighboring units is neither fixed nor are they ordered in generic graphs, thereby hindering the applications of convolutional operations. To enable model training on large-scale graphs, we propose a sub-graph training method to reduce the excessive memory and computational resource requirements suffered by prior methods on graph convolutions. Our results also indicate that the proposed methods using sub-graph training strategy are more efficient as compared to prior approaches.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Node Classification Citeseer LGCN sub Accuracy 73.0% # 1
Node Classification Cora LGCN sub Accuracy 83.30% # 1
Node Classification Pubmed LGCN sub Accuracy 79.50% # 2