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Graph Classification 벤치마크

Graph Classification on NEURON-BINARY

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Accuracy

80.1 82.65 85.2 87.75 90.3 2015-07 2026-09 PI-PL — 84.1 (2015-07-22) PI-PL — 84.1 (2015-07-22) SW — 85.1 (2017-06-11) SW — 85.1 (2017-06-11) PWGK — 80.1 (2017-06-12) PWGK — 80.1 (2017-06-12) WKPI-kmeans — 90.3 (2019-04-27) WKPI-kcenters — 86.5 (2019-04-27) WKPI-kmeans — 90.3 (2019-04-27) WKPI-kcenters — 86.5 (2019-04-27) PI-PL — 84.1 (2015-07-22) SW — 85.1 (2017-06-11) WKPI-kmeans — 90.3 (2019-04-27)
RankModel Accuracy PaperCodeYear
1 WKPI-kmeans 90.3 Learning metrics for persistence-based summaries and applications for graph classification topology474/WKPI 2019
2 WKPI-kcenters 86.5 Learning metrics for persistence-based summaries and applications for graph classification topology474/WKPI 2019
3 SW 85.1 Sliced Wasserstein Kernel for Persistence Diagrams 2017
4 PI-PL 84.1 Persistence Images: A Stable Vector Representation of Persistent Homology scikit-tda/persim · MathieuCarriere/perslay · CSU-TDA/PersistenceImages · +1 2015
5 PWGK 80.1 Kernel method for persistence diagrams via kernel embedding and weight factor genki-kusano/python-pwgk 2017
6 WKPI-kmeans 90.3 Learning metrics for persistence-based summaries and applications for graph classification topology474/WKPI 2019
7 WKPI-kcenters 86.5 Learning metrics for persistence-based summaries and applications for graph classification topology474/WKPI 2019
8 SW 85.1 Sliced Wasserstein Kernel for Persistence Diagrams 2017
9 PI-PL 84.1 Persistence Images: A Stable Vector Representation of Persistent Homology scikit-tda/persim · MathieuCarriere/perslay · CSU-TDA/PersistenceImages · +1 2015
10 PWGK 80.1 Kernel method for persistence diagrams via kernel embedding and weight factor genki-kusano/python-pwgk 2017
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