  Graph neural networks (GNNs) have emerged as a powerful tool for effectively
mining and learning from graph-structured data, with applications spanning
numerous domains. However, most research focuses on static graphs, neglecting
the dynamic nature of real-world networks where topologies and attributes
evolve over time. By integrating sequence modeling modules into traditional GNN
architectures, dynamic GNNs aim to bridge this gap, capturing the inherent
temporal dependencies of dynamic graphs for a more authentic depiction of
complex networks. This paper provides a comprehensive review of the fundamental
concepts, key techniques, and state-of-the-art dynamic GNN models. We present
the mainstream dynamic GNN models in detail and categorize models based on how
temporal information is incorporated. We also discuss large-scale dynamic GNNs
and pre-training techniques. Although dynamic GNNs have shown superior
performance, challenges remain in scalability, handling heterogeneous
information, and lack of diverse graph datasets. The paper also discusses
possible future directions, such as adaptive and memory-enhanced models,
inductive learning, and theoretical analysis.
