Quantifying and predicting evolutionary networks

The process of network evolution is typically characterized by the emergence of topological structures and the intricate interplay between determinism and stochasticity. Our work introduces a novel structural analysis framework to observe fluctuations in the network evolution process, profoundly influenced by the underlying network generation mechanisms. Based on theoretical reasoning and empirical examination, utilizing synthetic, static, and temporal networks, we arrive at two principal conclusions. Firstly, we reveal that the degree and distance distributions of networks exhibit two dynamic phenomena, convergence and divergence on high- and low-frequency curves, depend on the reflections of the network generation mechanism at both topological extreme values and the overall distribution. Secondly, we develop a novel link prediction method based on the convergence of topological fluctuations, which significantly outperforms benchmark algorithms in both real static and temporal networks. These findings are of considerable significance to the study of real network evolution mechanisms, contributing to a more comprehensive understanding of network behavior over time.