LLMs on Graphs

Three graph scenarios: Organizes the space by whether graphs are pure (no text), text-rich (nodes/edges carry natural language), or text-paired (graphs alongside documents).

Three role taxonomies: Categorizes LLMs as predictors, enhancers, or aligners with GNNs - clarifying whether the LLM is the model, a feature source, or a supervisor.

Task coverage: Spans node classification, link prediction, graph-level tasks, and reasoning over knowledge graphs.

Open problems: Flags scalability to large graphs, handling of graph structure without loss, and integration with tool-augmented LLMs as the key unsolved directions.