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)

PDF Abstract

Evaluation results from the paper

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

     Get a GitHub badge
Task Dataset Model Metric name Metric value Global rank Uses extra
training data
Node Classification Cora AS-GCN Accuracy 87.4% # 1