TSGNAS: A Topology- and Semantic-Guided Graph Neural Network Architecture Searcher.

Abstract

Designing effective graph neural networks (GNNs) for diverse tasks requires substantial manual effort, especially when dealing with the intricate interplay between topological structures and semantic information in graph data. Existing automated approaches often rely on off-the-shelf model combinations that remain superficial, resulting in generated model frameworks that are hardly interpretable and limited by the capabilities of the combined models. To address this gap, we propose an evolutionary-based framework that systematically searches for optimal GNN architectures while adaptively combining topological and semantic insights. This approach reduces human intervention, accommodates a wide range of graph tasks, and ensures robust performance in complex scenarios. Extensive experiments on real-world datasets demonstrate that our method outperforms both handcrafted GNNs and mainstream AutoML models in terms of accuracy, offering a scalable and adaptive solution for GNN design.