Subgraph Reasoning

Subgraph reasoning focuses on extracting and analyzing relevant substructures within larger graphs to perform complex reasoning tasks, such as question answering and knowledge graph completion. Current research emphasizes efficient subgraph retrieval methods, often employing graph neural networks (GNNs) and large language models (LLMs) to enhance reasoning capabilities and address scalability challenges in large knowledge graphs. This approach improves the accuracy and interpretability of predictions across diverse applications, including recommender systems and human trajectory prediction, while also tackling limitations of existing methods in handling out-of-distribution data and few-shot learning scenarios.