Linglin Yang's paper "NeuSO: Neural Optimizer for Subgraph Queries" on neural optimizer for subgraph queries has been accepted by SIGMOD 2026.

Subgraph query is a critical task in graph analysis with a wide range of applications across various domains. Most existing methods rely on heuristic vertex matching orderings, which may significantly degrade execution performance for certain queries. While learning-based optimizers have recently gained attention in the context of relational databases, they cannot be directly applied to subgraph queries. In this paper, we propose NeuSO, a novel learning-based optimizer for subgraph queries that achieves both high accuracy and efficiency. NeuSO features an efficient query graph encoder and an estimator which are trained using a multi-task framework to estimate both subquery cardinality and execution cost. Based on these estimates, NeuSO employs a top-down plan enumerator to generate high-quality execution plans for subgraph queries. Extensive experiments on multiple datasets demonstrate that NeuSO outperforms existing subgraph query ordering approaches in both performance and efficiency.