How Powerful are Graph Neural Networks?

Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes... (read more)

PDF Abstract ICLR 2019 PDF ICLR 2019 Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification BP-fMRI-97 GIN Accuracy 45.4% # 8
F1 42.3% # 8
Graph Classification CIFAR10 100k GIN Accuracy (%) 53.28 # 8
Graph Classification COLLAB GIN-0 Accuracy 80.2% # 11
Graph Classification HIV-DTI-77 GIN Accuracy 55.1% # 5
F1 53.6% # 6
Graph Classification HIV-fMRI-77 GIN Accuracy 52.5% # 6
F1 35.6% # 5
Graph Classification IMDb-B GIN-0 Accuracy 75.1% # 11
Graph Classification IMDb-M GIN-0 Accuracy 52.3% # 7
Graph Classification MUTAG GIN-0 Accuracy 89.4% # 17
Graph Classification NCI1 GIN-0 Accuracy 82.7% # 18
Node Classification PATTERN 100k GIN Accuracy (%) 85.590 # 4
Graph Classification PROTEINS GIN-0 Accuracy 76,2% # 70
Graph Classification PTC GIN-0 Accuracy 64.40% # 18
Graph Classification REDDIT-B GIN-0 Accuracy 92.4 # 3
Graph Classification RE-M5K GIN-0 Accuracy 57.5% # 1
Graph Regression ZINC-500k GIN MAE 0.526 # 15

Methods used in the Paper


METHOD TYPE
GraphSAGE
Graph Models
GIN
Graph Embeddings