MUTAG

Overview edit

Variants: MUTAG

Associated Benchmarks

This dataset is used in 2 benchmarks:

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

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis (2024) -
• PANDA: Expanded Width-Aware Message Passing Beyond Rewiring (2024) -
• Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns (2023) -
• Graph-level Representation Learning with Joint-Embedding Predictive Architectures (2023) -
• CIN++: Enhancing Topological Message Passing (2023) -
• SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks (2023) -
• Cell Attention Networks (2022) -
• TREE-G: Decision Trees Contesting Graph Neural Networks (2022) -
• DiffWire: Inductive Graph Rewiring via the Lovász Bound (2022) -
• Template based Graph Neural Network with Optimal Transport Distances (2022) -
• R-GCN: The R Could Stand for Random (2022) -
• DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (2021) -
• Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks (2021) -
• Accurate Learning of Graph Representations with Graph Multiset Pooling (2021) -
• Online Graph Dictionary Learning (2021) -
• Factorizable Graph Convolutional Networks (2020) -
• Wasserstein Embedding for Graph Learning (2020) -
• Segmented Graph-Bert for Graph Instance Modeling (2020) -
• Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (2019) -
• Universal Graph Transformer Self-Attention Networks (2019) -