Graph Reasoning

Graph reasoning focuses on developing computational methods that enable machines to understand and reason with graph-structured data, mirroring human abilities to infer relationships and solve problems based on interconnected information. Current research heavily utilizes large language models (LLMs), often augmented with techniques like multi-agent collaboration, pseudo-code prompting, and graph-centric instruction tuning, to improve accuracy and scalability in various graph reasoning tasks. This field is significant because it underpins advancements in numerous applications, including knowledge graph completion, question answering, and complex problem-solving in domains with inherent relational structures, such as social networks and biological systems.