A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History

The evolutionary dynamics of complex systems encode critical information about their functional organization. In particular, the generation times of edges reveal key aspects of historical development in networked systems such as protein-protein interaction networks, ecosystems, and social networks. Accurately recovering these temporal processes is of significant scientific value-for example, in elucidating the mechanisms underlying protein interaction evolution. However, existing methods typically assume access to partially time-stamped networks and often struggle to generalize across domains. They perform poorly in recovering edge generation times in static networks without temporal annotations. To address this challenge, we propose a comparative paradigm that enables cross-network learning by jointly training on multiple temporal networks. This framework captures structural-temporal correlations that generalize across networks and improves accuracy by 16.98% on average compared to separate training strategies. Furthermore, to mitigate the scarcity of real temporal data, we introduce a novel diffusion-based generative model for producing Augmented Temporal Networks (ATNs) . By integrating both real and generated samples during training, our joint strategy yields an additional 5.46% improvement in predictive accuracy, demonstrating the effectiveness of data augmentation in enhancing generalization.