Cornell

Overview edit

Variants: Cornell Grasp Dataset

Associated Benchmarks

This dataset is used in 1 benchmark:

• Node Classification - Metrics: Accuracy, Accuracy (%)

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• DeltaGNN: Graph Neural Network with Information Flow Control (2025) -
• Heterophilic Graph Neural Networks Optimization with Causal Message-passing (2024) -
• UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks (2024) -
• The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs (2024) -
• Graph Neural Reaction Diffusion Models (2024) -
• Transfer Entropy in Graph Convolutional Neural Networks (2024) -
• Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs (2024) -
• Graph Neural Aggregation-diffusion with Metastability (2024) -
• Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network (2024) -
• Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks (2023) -
• GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy (2023) -
• CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph (2023) -
• Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification (2023) -
• Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity (2023) -
• UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification (2023) -
• Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters (2023) -
• Self-attention Dual Embedding for Graphs with Heterophily (2023) -
• GCNH: A Simple Method For Representation Learning On Heterophilous Graphs (2023) -
• Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing (2023) -
• Beyond Homophily with Graph Echo State Networks (2022) -