NCI1

Overview edit

Variants: NCI1, NCI-123

Associated Benchmarks

This dataset is used in 1 benchmark:

• Graph Classification - Metrics: Accuracy, Accuracy (10-fold)

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck (2024) -
• An end-to-end attention-based approach for learning on graphs (2024) -
• Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity (2023) -
• Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes (2023) -
• CIN++: Enhancing Topological Message Passing (2023) -
• Strengthening structural baselines for graph classification using Local Topological Profile (2023) -
• Cell Attention Networks (2022) -
• TREE-G: Decision Trees Contesting Graph Neural Networks (2022) -
• Pure Transformers are Powerful Graph Learners (2022) -
• Template based Graph Neural Network with Optimal Transport Distances (2022) -
• Recipe for a General, Powerful, Scalable Graph Transformer (2022) -
• A New Perspective on the Effects of Spectrum in Graph Neural Networks (2021) -
• DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (2021) -
• Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks (2021) -
• Do Transformers Really Perform Bad for Graph Representation? (2021) -
• How Attentive are Graph Attention Networks? (2021) -
• A Novel Higher-order Weisfeiler-Lehman Graph Convolution (2020) -
• Wasserstein Embedding for Graph Learning (2020) -
• Principal Neighbourhood Aggregation for Graph Nets (2020) -
• A Fair Comparison of Graph Neural Networks for Graph Classification (2019) -