Boosting with Fewer Tokens: Multi-Query Optimization for LLMs Using Node Text and Neighbor Cues

Recent studies have explored querying large language models (LLMs) to serve as predictors for graph mining tasks on text-attributed graphs (TAGs), establishing a promising paradigm that surpasses Graph Neural Networks (GNNs) in scalability and generalization. However, the high token costs of LLMs make this approach prohibitively expensive for large-scale node queries, and effective multi-query optimization solutions are currently lacking. By conducting information theory analysis at the single query level, we have gained insights that enabled the development of two multi-query optimization strategies: token pruning and query boosting. The token pruning strategy is designed to reduce token usage without compromising task performance by identifying saturated node queries and pruning tokens for these queries. Meanwhile, the query boosting strategy is designed to enhance task performance by enriching the context of unexecuted queries with pseudo-labels derived from previous queries through strategic scheduling, thereby maximizing the utility of these pseudo-labels. Extensive experiments applying these two strategies, either jointly or individually, to various existing methods demonstrate that the proposed approach serves our intentions well. Besides, this paper offers a fresh methodology for optimizing LLM processing of graph tasks, demonstrating great potential. For most natural graph data benchmarks in the field, it can save tokens by several orders of magnitude. For example, on the Ogbn-Products dataset, it could theoretically save up to $2\times 10^{9}$ tokens.