Hierarchical Graph Pooling with Structure Learning

14 Nov 2019 Zhen Zhang Jiajun Bu Martin Ester Jianfeng Zhang Chengwei Yao Zhi Yu Can Wang

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification D&D HGP-SL Accuracy 80.96% # 8
Graph Classification ENZYMES HGP-SL Accuracy 68.79% # 5
Graph Classification Mutagenicity HGP-SL Accuracy 82.15 # 1
Graph Classification NCI1 HGP-SL Accuracy 78.45% # 25
Graph Classification NCI109 HGP-SL Accuracy 80.67 # 9
Graph Classification PROTEINS HGP-SL Accuracy 84.91% # 1

Methods used in the Paper


METHOD TYPE
Convolution
Convolutions