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NutriGNN: Food Nutrient Prediction with GNNs

A graph neural network approach to predicting missing nutrient values in food composition databases. By building a knowledge graph enriched with LLM-derived semantic relations between foods, NutriGNN improves representation learning and prediction quality, especially for low-resource food items with sparse nutritional data.

Developed for a graduate course on Graphs and GNNs with Dr. Ambuj K. Singh.