ABG-NAS: Adaptive bayesian genetic neural architecture search for graph representation learning

作者：Sixuan Wang, Jiao Yin, Jinli Cao, MingJian Tang, Hua Wang, Yanchun Zhang · 发表于：Knowledge-Based Systems · 年份：2025 · DOI：10.1016/j.knosys.2025.114235 · 被引用次数：5 · 研究领域：Advanced Graph Neural Networks、Graph Theory and Algorithms、Multimodal Machine Learning Applications

• We propose ABG-NAS, an adaptive NAS framework for graph representation learning. • A novel genetic search strategy dynamically balances exploration and exploitation. • Bayesian optimization is embedded to tune hyperparameters during the search process. • Our method outperforms SOTA GNAS methods on four benchmark graph datasets. • ABG-NAS achieves high F1 scores on both sparse and dense real-world graph structures. Effective and efficient graph representation learning is essential for enabling critical downstream tasks, such as node classification, link prediction, and subgraph search. However, existing graph neural network (GNN) architectures often struggle to adapt to diverse and complex graph structures, limiting their ability to produce structure-aware and task-discriminative representations. To address this challenge, we propose ABG-NAS, a novel framework for automated graph neural network architecture search tailored for efficient graph representation learning. ABG-NAS encompasses three key components: a Comprehensive Architecture Search Space (CASS), an Adaptive Genetic Optimization Strategy (AGOS), and a Bayesian-Guided Tuning Module (BGTM). CASS systematically explores diverse propagation ( P ) and transformation ( T ) operations, enabling the discovery of GNN architectures capable of capturing intricate graph characteristics. AGOS dynamically balances exploration and exploitation, ensuring search efficiency and preserving solution diversity. BGTM further optimizes ...