Dynamic Representation Learning via Recurrent Graph Neural Networks

• Published in: IEEE transactions on systems, man, and cybernetics. Systems man, and cybernetics. Systems, 2023-02, Vol.53 (2), p.1284-1297
• Main Authors: Zhang, Chun-Yang, Yao, Zhi-Liang, Yao, Hong-Yu, Huang, Feng, Chen, C. L. Philip
• Format: Article
• Language: English
• Subjects: Biological system modeling; Computational modeling; Dynamic graphs; Evolution; Feature extraction; Graph neural networks; graph neural networks (GNNs); graph representation learning (GRL); Graph representations; Graph theory; Graphical representations; Graphs; Matrix decomposition; Network analysis; Network topologies; Neural networks; node embeddings; Nodes; recurrent neural network (RNN); Recurrent neural networks; Representation learning; State of the art; Structured data; Task analysis
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2022.3196506

A large number of real-world systems generate graphs that are structured data aligned with nodes and edges. Graphs are usually dynamic in many scenarios, where nodes or edges keep evolving over time. Recently, graph representation learning (GRL) has received great success in network analysis, which aims to produce informative and representative features or low-dimensional embeddings by exploring node attributes and network topology. Most state-of-the-art models for dynamic GRL are composed of a static representation learning model and a recurrent neural network (RNN). The former generates the representations of a graph or nodes from one static graph at a discrete time step, while the latter captures the temporal correlation between adjacent graphs. However, the two-stage design ignores the temporal dynamics between contiguous graphs during the learning processing of graph representations. To alleviate this problem, this article proposes a representation learning model for dynamic graphs, called DynGNN. Differently, it is a single-stage model that embeds an RNN into a graph neural network to produce better representations in a compact form. This takes the fusion of temporal and topology correlations into account from low-level to high-level feature learning, enabling the model to capture more fine-grained evolving patterns. From the experimental results on both synthetic and real-world networks, the proposed DynGNN yields significant improvements in multiple tasks compared to the state-of-the-art counterparts.