Hyperbolic Graph Convolutional Neural Networks

NeurIPS 2019 • Ines Chami • Rex Ying • Christopher Ré • Jure Leskovec

Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space, which has been shown to incur a large distortion when embedding real-world graphs with scale-free or hierarchical structure. Hyperbolic geometry offers an exciting alternative, as it enables embeddings with much smaller distortion... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Link Prediction PPI HGCN Accuracy 84.5 # 1