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Models/CKGCN

CKGCN

Reported on 11 benchmarks across 4 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Graphs8 results

Graph RegressiononPeptides-struct
MAE· 2024-04-21
0.2477
best: 0.2432 (ECFP + LightGBM)
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Graph ClassificationonMNIST
Accuracy· 2024-04-21
98.423
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Graph ClassificationonCIFAR-10
Accuracy· 2024-04-21
72.785
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Node ClassificationonPATTERN
Accuracy· 2024-04-21
88.661
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Graph RegressiononZINC
MAE· 2024-04-21
0.059
best: 0.051 (ESA + rings + NodeRWSE + EdgeRWSE)
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Graph RegressiononZINC-500k
MAE· 2024-04-21
5.9
best: 0.051 (ESA + rings + NodeRWSE + EdgeRWSE)
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Graph ClassificationonPeptides-func
AP· 2024-04-21
0.6952
best: 0.7479 (ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set))
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
Node ClassificationonCLUSTER
Accuracy· 2024-04-21
79.003
best: 80.026 (GRIT)
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604

Methodology3 results

ClassificationonMNIST
Accuracy· 2024-04-21
98.423
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
ClassificationonCIFAR-10
Accuracy· 2024-04-21
72.785
SOTA
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604
ClassificationonPeptides-func
AP· 2024-04-21
0.6952
best: 0.7479 (ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set))
CKGConv: General Graph Convolution with Continuous Kernels arXiv:2404.13604