Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Node Classification Amazon2M Cluster-GCN F1 90.41 # 1
Node Classification PPI ClusterGCN F1 92.9 # 14
Node Classification PPI Cluster-GCN F1 99.36 # 7
Node Classification Pubmed ClusterGCN F1 79.9 # 1

Methods used in the Paper


METHOD TYPE
GCN
Graph Models