3 articles tagged with #text-attributed-graphs. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose ERAlign, an energy-based framework that aligns representations from Graph Neural Networks and Large Language Models when processing text-attributed graphs. The approach uses energy-based models to achieve distribution consistency between graph structure and text embeddings, demonstrating state-of-the-art performance across multiple datasets.
Researchers propose GNN-as-Judge, a framework combining Large Language Models with Graph Neural Networks to improve learning on text-attributed graphs in low-resource settings. The approach uses collaborative pseudo-labeling and weakly-supervised fine-tuning to generate reliable labels while reducing noise, demonstrating significant performance gains when labeled data is scarce.
Researchers propose LEMP4HG, a new language model-enhanced approach for improving graph neural networks on heterophilic graphs where connected nodes have different characteristics. The method leverages language models to better understand semantic relationships between text-attributed nodes, outperforming existing methods while maintaining efficiency through selective message enhancement.
