IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification

22 Jul 2019 Lin Meng Jiawei Zhang

Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification BP-fMRI-97 IsoNN Accuracy 64.9% # 1
F1 69.7% # 1
Graph Classification BP-fMRI-97 IsoNN-fast Accuracy 62.3% # 3
F1 63.2% # 4
Graph Classification HIV-DTI-77 IsoNN Accuracy 67.5% # 1
F1 68.3% # 1
Graph Classification HIV-DTI-77 IsoNN-fast Accuracy 60.1% # 3
F1 61.9% # 3
Graph Classification HIV-fMRI-77 IsoNN Accuracy 73.4 # 1
F1 72.2 # 1
Graph Classification HIV-fMRI-77 IsoNN-Fast Accuracy 70.5% # 2
F1 69.9% # 2
Graph Classification HIV-fMRI-77 IsoNN Accuracy 73.4% # 1
F1 72.2% # 1
Graph Classification MUTAG Function Space Pooling Accuracy 83.3% # 48
Graph Classification PTC IsoNN Accuracy 59.9% # 29

Methods used in the Paper


METHOD TYPE
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