SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs
#SaVe-TAG #Graph Neural Networks #Long-tailed distributions #Text-attributed graphs #Vicinal Risk Minimization #LLM-based interpolation #Class imbalance #Semantic information
📌 Key Takeaways
- SaVe-TAG introduces LLM-based interpolation for long-tailed text-attributed graphs
- The method addresses GNNs' poor generalization across head and tail classes
- Existing VRM approaches using embedding-space arithmetic fail to capture rich semantics
- This advancement could improve performance in applications using text-attributed graphs
📖 Full Retelling
Researchers introduced SaVe-TAG, a novel LLM-based interpolation method for long-tailed text-attributed graphs, in a paper published on arXiv (2410.16882v5) in October 2024, addressing the challenge of Graph Neural Networks' poor generalization across head and tail classes in real-world graph data that follows long-tailed distributions. The paper highlights that while recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance through numeric interpolation, existing approaches primarily rely on embedding-space arithmetic, which fails to capture the rich semantic information inherent in text-attributed graphs. This limitation becomes particularly problematic when dealing with the imbalanced distributions commonly found in real-world graph data, where certain classes (head classes) have significantly more instances than others (tail classes). The SaVe-TAG methodology represents an innovative approach that leverages Large Language Models (LLMs) to perform more sophisticated interpolation in the text domain rather than just in the embedding space, potentially leading to more balanced performance across both head and tail classes.
🏷️ Themes
Graph Neural Networks, Class Imbalance, Text-Attributed Graphs
📚 Related People & Topics
Graph neural network
Class of artificial neural networks
Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular drug design. Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the...
Entity Intersection Graph
Connections for Graph neural network:
🌐
Mixture of experts
1 shared
🌐
Network analysis
1 shared
🌐
Echo (disambiguation)
1 shared
🌐
Recommender system
1 shared
🌐
Information retrieval
1 shared
Original Source
arXiv:2410.16882v5 Announce Type: replace
Abstract: Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance with numeric interpolation; however, existing approaches largely rely on embedding-space arithmetic, which fails to capture the rich semantics inherent in text-attributed graphs
Read full article at source