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FastAlign presents a scalable, sparsity-aware framework for optimal transport-based network alignment, achieving state-of-the-art accuracy while reducing runtime by up to 9.45x on CPU and 32.54x on GPU.
This survey reviews the use of large language models for graph computation, categorizing them into two paradigms: LLMs as executors and LLMs as planners. It finds LLMs promising for simple tasks but unreliable for large-scale exact computations, and suggests future directions.