Dynamical system for PageRank with a time-dependent memory
2026-07-20 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors extend the PageRank model by allowing the personalization vector (which influences rankings) to change over time based on its past values weighted by a memory function. They formulate this as a mathematical problem involving integro-differential equations and prove that, for well-connected networks, the rankings stabilize regardless of the starting point when using many types of memory functions. However, when the memory function oscillates exponentially, the rankings show repeating cycles instead of settling down. Their work ensures that the model always produces valid probability rankings throughout its evolution.
PageRankpersonalization vectormemory functionintegro-differential equationstationary statestrongly connected networkexponential-oscillatory functionasymptotic behaviorprobability vectordynamical systems
Authors
Julio Flores, Eva Primo, Daniel Rodríguez, Miguel Romance
Abstract
Inspired by the dynamical PageRank framework of Gleich and Rossi in 2012, we introduce a continuous-time PageRank model in which the personalization vector evolves as a weighted average of its past values, with the weights determined by a memory function. The resulting dynamics are formulated as an initial value problem for an integro-differential equation, where the initial condition is a probability vector. We investigate how the choice of memory function influences the long-time behavior of the PageRank vector. In particular, for strongly connected networks $\mathcal{G}$, we prove that broad classes of memory functions lead to convergence toward a stationary state that is independent of the initial condition. In contrast, when the memory function is exponential-oscillatory, $ω(t)=e^{at}\cos(bt)$ for $t\geq0$ with $a,b>0$, we show that the PageRank dynamics exhibit asymptotically periodic behavior, revealing that oscillatory memory can fundamentally alter the qualitative evolution of the ranking process. To establish these results, we first prove the existence and uniqueness of solutions using standard results from the theory of integro-differential equations and show that the solution remains a probability vector for all times, thereby preserving the essential properties of the PageRank model.