[2603.04545] An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs

Computer Science > Machine Learning arXiv:2603.04545 (cs) [Submitted on 4 Mar 2026] Title:An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs Authors:Waleed Afandi, Hussein Abdallah, Ashraf Aboulnaga, Essam Mansour View a PDF of the paper titled An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs, by Waleed Afandi and 3 other authors View PDF HTML (experimental) Abstract:Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference queries are computationally expensive and vary in complexity, as each involves a different number of target nodes linked to subgraphs of diverse densities and structures. Existing acceleration methods, such as pruning, quantization, and knowledge distillation, instantiate smaller models but do not adapt them to the structure or semantics of individual queries. They also store models as monolithic files that must be fully loaded, and miss the opportunity to retrieve only the neighboring nodes and corresponding model components that are semantically relevant to the target nodes. These limitations lead to excessive data loading and redundant computation on large KGs. This paper presents KG-WISE, a task-driven inference paradigm for large KGs. KG-WISE decomposes trained GNN models into fine-grained components that can be partially loaded based on the structure of the queried subgraph. It employs large la...