Wasserstein Embedding for Graph Learning

We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity between their node embedding distributions... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification COLLAB WEGL Accuracy 79.8% # 11
Graph Classification D&D WEGL Accuracy 78.6% # 19
Graph Classification ENZYMES WEGL Accuracy 60.5 # 15
Graph Classification IMDb-B WEGL Accuracy 75.4% # 8
Graph Classification IMDb-M WEGL Accuracy 52% # 7
Graph Classification MUTAG WEGL Accuracy 88.3% # 27
Graph Classification NCI1 WEGL Accuracy 76.8% # 25
Graph Classification ogbg-molhiv WEGL ROC-AUC 77.6 # 1
Graph Classification PROTEINS WEGL Accuracy 76.5% # 22
Graph Classification PTC WEGL Accuracy 67.5% # 11
Graph Classification REDDIT-B WEGL Accuracy 92 # 4
Graph Classification RE-M12K WEGL Accuracy 47.8% # 4
Graph Classification RE-M5K WEGL Accuracy 55.1% # 3

Methods used in the Paper