Adaptive Sampling Towards Fast Graph Representation Learning

NeurIPS 2018 Wenbing HuangTong ZhangYu RongJunzhou Huang

Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers... (read more)

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Evaluation results from the paper


 SOTA for Node Classification on Cora (using extra training data)

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Node Classification Cora AS-GCN Accuracy 87.4% # 1