Mutual Information Maximization in Graph Neural Networks

21 May 2019Xinhan DiPengqian YuRui BuMingchao Sun

A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme to learn the representation of nodes... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Graph Classification 20NEWS sKNN-LDS Accuracy 47.9 # 1
Graph Classification Cancer sKNN-LDS Accuracy 95.7 # 1
Link Prediction Citeseer sGraphite-VAE AUC 94.1% # 3
AP 95.4% # 1
Graph Classification Citeseer sKNN-LDS Accuracy 73.7 # 1
Graph Classification COLLAB sGIN Accuracy 80.71% # 7
Graph Classification Cora sKNN-LDS Accuracy 72.3 # 1
Link Prediction Cora sGraphite-VAE AUC 93.7% # 2
AP 93.5% # 2
Graph Classification Digits sKNN-LDS Accuracy 92.5 # 1
Graph Classification IMDb-B sGIN Accuracy 77.94% # 5
Graph Classification IMDb-M sGIN Accuracy 54.52% # 5
Graph Classification MUTAG sGIN Accuracy 94.14% # 3
Graph Classification NCI1 sGIN Accuracy 83.85% # 10
Graph Classification PROTEINS sGIN Accuracy 78.97% # 3
Graph Classification PTC sGIN Accuracy 73.56% # 5
Link Prediction Pubmed sGraphite-VAE AUC 94.8% # 3
AP 96.3% # 2
Graph Classification Wine sKNN-LDS Accuracy 98 # 1

Methods used in the Paper


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