Graph Reasoning

Graph reasoning studies how to use entities and their relationships to answer questions, infer missing links, analyze structure, and guide decisions in settings such as knowledge graphs, scene graphs, and social or scientific networks. Approaches include graph neural networks and message passing, symbolic path search, and language-model agents that retrieve, decompose, or execute graph operations with tools. These methods support tasks from multi-hop knowledge-graph question answering to visual navigation and traffic-scene understanding, where explicit structure can make evidence and decisions easier to trace. A central challenge is reliability at scale: performance often falls as graphs or reasoning chains grow, and results depend on how graph information is represented and whether models can execute exact operations rather than merely describe them.