Graph Classification On Neuron Average
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| PWGK | 62.80 | Kernel method for persistence diagrams via kernel embedding and weight factor | - |
| PI-PL | 64.20 | Persistence Images: A Stable Vector Representation of Persistent Homology | - |
| WKPI-kcenters | 77.80 | Learning metrics for persistence-based summaries and applications for graph classification | - |
| WKPI-kmeans | 73.50 | Learning metrics for persistence-based summaries and applications for graph classification | - |
| SW | 71.20 | Sliced Wasserstein Kernel for Persistence Diagrams | - |
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