Graphs as a new frontier for large language models

Large language models like ChatGPT are expanding their capabilities beyond language tasks and are being explored for their potential in solving graph-related problems. Graphs, consisting of nodes and edges, are used in various domains such as social network analysis. The challenge lies in converting graph data into a format that can be processed by large language models. Graph inductive bias, which allows useful information to be learned from one graph and applied to another, plays a crucial role. The distinction between homophilic and heterophilic graphs, where nodes tend to connect with similar or dissimilar nodes, further influences the transferability of knowledge between domains.