PTC

Overview edit

Variants: PTC

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:

• CIN++: Enhancing Topological Message Passing (2023) -
• Cell Attention Networks (2022) -
• TREE-G: Decision Trees Contesting Graph Neural Networks (2022) -
• Template based Graph Neural Network with Optimal Transport Distances (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) -
• Wasserstein Embedding for Graph Learning (2020) -
• Segmented Graph-Bert for Graph Instance Modeling (2020) -
• Universal Graph Transformer Self-Attention Networks (2019) -
• Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning (2019) -
• InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (2019) -
• IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification (2019) -
• Graph Representation Learning via Hard and Channel-Wise Attention Networks (2019) -
• Wasserstein Weisfeiler-Lehman Graph Kernels (2019) -
• Provably Powerful Graph Networks (2019) -
• Mutual Information Maximization in Graph Neural Networks (2019) -
• DDGK: Learning Graph Representations for Deep Divergence Graph Kernels (2019) -
• DAGCN: Dual Attention Graph Convolutional Networks (2019) -
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019) -
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019) -