HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs

arXiv:2503.04822 · cs.CL, cs.AI · Submitted 2025-03-05 · Read on arXiv

cs.CL, cs.AI

Submitted: 2025-03-05

Updated: 2026-08-30

Code: https://github.com/honey0219/HeTGB

Terminology

• Benchmark — 40× in this paper · explained in 2 episodes
• Actor — 39× in this paper · explained in 3 episodes
• GNNs — 38× in this paper · explained in 2 episodes
• Embeddings — 9× in this paper · explained in 2 episodes
• Aggregation — 8× in this paper · explained in 1 episode
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Benchmarking — 6× in this paper · explained in 3 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Representation Learning — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Standardized Benchmarks — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions
• GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking
• Semi-Supervised Classification with Graph Convolutional Networks
• A Survey of Graph Meets Large Language Model: Progress and Future Directions
• TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
• Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis
• Graph Foundation Models: Concepts, Opportunities and Challenges
• LLaMA: Open and Efficient Foundation Language Models
• How Powerful are Graph Neural Networks?
• Non-Homophilic Graph Pre-Training and Prompt Learning
• DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
• Graph Neural Networks for Graphs with Heterophily: A Survey

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