文献类型：期刊文献

英文题名：Research Progress and Frontier Applications of Graph Neural Networks

作者：Liu, Zhenyu[1,2]; Yao, Dengfeng[1,2,3,4]

第一作者：Liu, Zhenyu

机构：[1] Beijing Key Lab of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China; [3] Lab of Computational Linguistics, School of Humanities, Tsinghua University, Beijing, 100084, China; [4] Center for Psychology and Cognitive Science, Tsinghua University, Beijing, 100084, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2026

卷号：2774 CCIS

起止页码：319-336

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20260920158347)

语种：英文

外文关键词：Anomaly detection - Contrastive Learning - Data fusion - Data handling - Graph neural networks - Graph theory - Graphic methods - Learning systems - Modal analysis - Multi agent systems

摘要：This paper focuses on the research progress and application exploration of graph neural networks (GNNs) in handling heterogeneous graphs, multimodal data, and practical applications. It summarizes various innovative model architectures and learning strategies addressing issues such as semantic aggregation in heterogeneous graphs, representation learning driven by contrastive learning, spectral domain reconstruction, and dynamic graph structure learning in multimodal data. This paper also reviews recent research achievements in practical application problems such as defense against adversarial attacks, calibration of prediction uncertainty, and few-shot learning. Additionally, it explores the wide range of applications of GNNs in fields such as dynamic spatiotemporal data modelling, anomaly detection in multivariate time series, cross-modal data fusion, distributed decision-making in multiagent systems, link prediction and stream data processing, recommendation systems, fake news detection, open-set recognition of mental disorders, avatar rendering, and planning tasks. This paper further analyses the technical trend of integrating GNNs with transformers and discusses current research challenges, such as model scalability, computational efficiency, and the generalizability of defenses against adversarial attacks. It also provides an outlook on future research directions. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.