NCI109

Overview edit

Variants: NCI109

Associated Benchmarks

This dataset is used in 1 benchmark:

• Graph Classification - Metrics: Accuracy

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) -
• Cell Attention Networks (2022) -
• Pure Transformers are Powerful Graph Learners (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) -
• Do Transformers Really Perform Bad for Graph Representation? (2021) -
• How Attentive are Graph Attention Networks? (2021) -
• Principal Neighbourhood Aggregation for Graph Nets (2020) -
• ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (2019) -
• Hierarchical Graph Pooling with Structure Learning (2019) -
• Provably Powerful Graph Networks (2019) -
• Graph Convolutional Networks with EigenPooling (2019) -
• Self-Attention Graph Pooling (2019) -
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019) -
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019) -