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Models/CIN++

CIN++

Reported on 7 benchmarks across 3 tasks · 1 paper · 2 SOTA

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

Graphs4 results

Graph ClassificationonHIV dataset
ROC-AUC· 2023-06-06
80.63
SOTA
CIN++: Enhancing Topological Message Passing arXiv:2306.03561
Graph RegressiononZINC
MAE· 2023-06-06
0.074
best: 0.051 (ESA + rings + NodeRWSE + EdgeRWSE)
CIN++: Enhancing Topological Message Passing arXiv:2306.03561
Graph ClassificationonNCI109
Accuracy· 2023-06-06
84.5
best: 87.3 (WKPI-kcenters)
CIN++: Enhancing Topological Message Passing arXiv:2306.03561
Graph ClassificationonPROTEINS
Accuracy· 2023-06-06
80.5
best: 84.91 (HGP-SL)
CIN++: Enhancing Topological Message Passing arXiv:2306.03561

Methodology3 results

ClassificationonHIV dataset
ROC-AUC· 2023-06-06
80.63
SOTA
CIN++: Enhancing Topological Message Passing arXiv:2306.03561
ClassificationonNCI109
Accuracy· 2023-06-06
84.5
best: 87.3 (WKPI-kcenters)
CIN++: Enhancing Topological Message Passing arXiv:2306.03561
ClassificationonPROTEINS
Accuracy· 2023-06-06
80.5
best: 84.91 (HGP-SL)
CIN++: Enhancing Topological Message Passing arXiv:2306.03561