Network evolution with Macroscopic Delays: asymptotics and condensation

Sayan Banerjee, Shankar Bhamidi, Partha Dey, Akshay Sakanaveeti
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Abstract

Driven by the explosion of data and the impact of real-world networks, a wide array of mathematical models have been proposed to understand the structure and evolution of such systems, especially in the temporal context. Recent advances in areas such as distributed cyber-security and social networks have motivated the development of probabilistic models of evolution where individuals have only partial information on the state of the network when deciding on their actions. This paper aims to understand models incorporating \emph{network delay}, where new individuals have information on a time-delayed snapshot of the system. We consider the setting where one has macroscopic delays, that is, the ``information'' available to new individuals is the structure of the network at a past time, which scales proportionally with the current time and vertices connect using standard preferential attachment type dynamics. We obtain the local weak limit for the network as its size grows and connect it to a novel continuous-time branching process where the associated point process of reproductions \emph{has memory} of the entire past. A more tractable `dual description' of this branching process using an `edge copying mechanism' is used to obtain degree distribution asymptotics as well as necessary and sufficient conditions for condensation, where the mass of the degree distribution ``escapes to infinity''. We conclude by studying the impact of the delay distribution on macroscopic functionals such as the root degree.
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具有宏观延迟的网络演化:渐进与凝聚
在数据爆炸和现实世界网络影响的推动下,人们提出了大量数学模型来理解这类系统的结构和演化,特别是在时间方面。分布式网络安全和社交网络等领域的最新进展推动了概率演化模型的发展,在这些模型中,个体在决定自己的行动时只掌握了网络状态的部分信息。本文旨在理解包含 "网络延迟"(emph{networkdelay})的模型,在这种模型中,新个体拥有关于系统时间延迟快照的信息。我们考虑了具有宏观延迟的情况,即新个体所能获得的 "信息 "是过去某个时间的网络结构,该结构与当前时间成比例,并使用标准的优先附着型动力学进行连接。随着网络规模的增长,我们得到了网络的局部弱极限,并将其与一个新颖的连续时间分支过程联系起来,在这个过程中,相关的再生产点过程(emph{has memory})具有对整个过去的记忆。我们利用 "边复制机制 "对这一分支过程进行了更为简洁的 "描述",从而得到了度分布渐近线以及凝聚的必要条件和充分条件,在凝聚过程中,度分布的质量 "逃逸到无穷大"。最后,我们研究了延迟分布对宏观函数(如根度)的影响。
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