arXiv:2606.06865v1 Announce Type: new Abstract: Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algorithmic operations. Yet, it remains unclear when LLMs can reliably support such computation and how they should be incorporated into graph-solving pipelines. Existing surveys at the intersection of LLMs and graphs primarily focus on graph learning, text-attributed graphs, or graph-language modeling. To bridge this gap, we provide a comprehensive review of LLMs for graph computation through a role-
The rapid advancement and widespread application of LLMs are pushing researchers to explore their capabilities in increasingly complex computational domains, such as graph computation, where traditional methods face limitations.
This research is critical for understanding the reliable boundaries and integration strategies of LLMs in structured data environments, directly impacting the development of more complex and autonomous AI systems.
The ability of LLMs to perform graph computation changes how complex relationships and algorithmic operations can be processed, potentially enabling new analytical tools and AI agent capabilities.
- · AI researchers
- · Graph database companies
- · Data scientists
- · Software developers
- · Traditional graph computation software reliant on manual feature engineering
- · Companies slow to adopt LLM integration
LLMs will be increasingly integrated into systems requiring reasoning over structured relationships.
This integration could lead to significant improvements in areas like fraud detection, drug discovery, and social network analysis.
The enhanced capability of LLMs in graph computation could accelerate the development of advanced AI agents that can deeply understand and manipulate complex real-world data structures.
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