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


#2 best model for Node Classification on Cora (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
COMPARE
Node Classification Citeseer Full-supervised ASGCN Accuracy 79.66% # 2
Node Classification Cora AS-GCN Accuracy 87.4% # 2
Node Classification Cora Full-supervised ASGCN Accuracy 87.44% # 2
Node Classification Pubmed Full-supervised ASGCN Accuracy 90.60% # 2
Node Classification Reddit ASGCN Accuracy 96.27% # 3