
Leveraging large language models (LLMs) for text-based data augmentation significantly improves imbalanced node classification performance in text-attributed graphs, outperforming traditional methods.


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llm-based-data-augmentation-improves-imbalanced-node-classification-in-text-attributed-graphs-


LLM-Based Data Augmentation Improves Imbalanced Node Classification in Text-Attributed Graphs


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This paper introduces ECGN, a novel graph neural network architecture that excels in imbalanced node classification by effectively capturing local graph structures within clusters and addressing class imbalance through a novel synthetic node generation technique.


enhanced-cluster-aware-graph-networks-for-imbalanced-node-classification-addressing-class-imbalance-and-local-structure-learning-in-graphs


Enhanced Cluster-aware Graph Networks for Imbalanced Node Classification: Addressing Class Imbalance and Local Structure Learning in Graphs



Graffin, a novel pluggable module, enhances tail data performance in imbalanced node classification tasks without significantly degrading overall model performance.


graffin-enhancing-tail-data-performance-in-imbalanced-node-classification-tasks


Graffin: Enhancing Tail Data Performance in Imbalanced Node Classification Tasks
