A Comprehensive Study of Graph Neural Networks: Types and Application

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This systematic review presents a comprehensive analysis of graph neural networks (GNNs), focusing on their major types and applications. GNNs have emerged as an effective deep learning (DL) method for modelling and analysing intricate relationships in graph‐structured data. Their versatility has led to widespread adoption across diverse domains and tasks. This review examines a wide range of GNN models and their applications, analysing their strengths and limitations. A total of 96 research papers published between 2019 and 2025 are systematically reviewed, providing a comprehensive overview of the development and application of GNN‐based methods. Despite the remarkable success of GNNs across diverse application domains, existing models still face several challenges, including scalability, high computational complexity, limited interpretability, and inadequate generalisation to heterogeneous and evolving data environments. Future research should focus on developing lightweight, explainable, and privacy‐preserving GNN architectures, integrating self‐supervised and multimodal learning strategies, and designing robust frameworks capable of handling dynamic and large‐scale graph data. This review provides a valuable reference for practitioners and researchers in the field of GNNs, supporting future research and application development.