LLMs on Graphs
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A comprehensive overview of the many ways LLMs can be applied to graph-structured data and when each pattern is useful.
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.
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