Beyond TimeGraph: A Comparative Analysis of Temporal Generators for Evolving Network Graphs

The growing adoption of Artificial Intelligence (AI) in network and service management demands extensive, diverse, and high-fidelity datasets for training and evaluation. However, collecting real-world network data at scale often faces significant challenges, including privacy concerns, operational constraints, and the rarity of certain events or conditions. Generative AI offers a promising solution by synthesizing realistic data that mirrors complex network dynamics and user behavior.In many application domains-such as mobile connectivity, cybersecurity, and disaster recovery-realism is not only defined by accurate replication of structural features (e.g., connectivity graphs), but also by the ability to model how these features evolve over time. Capturing these temporal dynamics is critical to ensure that AI models trained on synthetic data can generalize effectively to real-world scenarios. One effective approach to this challenge is to transform raw graph data into a compact latent representation, which can then be processed by a temporal generative model. This two-stage framework enables the learning of both structural and temporal characteristics of the underlying system, offering a more comprehensive generative pipeline.Building on previous work that employed Time-series Generative Adversarial Networks (TimeGAN) for this purpose, this paper explores an alternative temporal generative model: DoppelGANger. By integrating DoppelGANger into the graph generation pipeline, we aim to assess whether it can more accurately capture the dynamics of evolving graph structures. Furthermore, we introduce a more rigorous and detailed evaluation of the generated data by comparing decoded synthetic graph sequences against their real-world counterparts using distribution-aware and graph-structural metrics. These metrics provide a clearer picture of the quality and fidelity of the generated data, highlighting key differences between the TimeGAN and DoppelGANger approaches.