Temporal connection probabilities in real networks
Provides a principled, interpretable foundation for probabilistic link prediction in temporal networks, addressing a fundamental problem in network science.
Derived a closed-form non-Markovian expression for next-step connection probabilities in temporal networks, unifying hyperbolic geometry and long-range memory. Achieved quantitative agreement with empirical data from large-scale real networks.
Principled prediction of when and where links form in complex networks is a fundamental problem. We derive a closed-form non-Markovian expression for next-step connection probabilities that unifies latent hyperbolic geometry with long-range memory of past interactions. This expression yields interpretable forecasts governed by a small set of parameters. Applied to large-scale real networks, we find quantitative agreement with empirical connection probabilities and reveal how geometry and memory jointly shape link dynamics. These results establish a minimal and extensible foundation for principled probabilistic forecasting of temporal network topology.