Analyzing Social Networks As Stochastic Processes
提出一种基于连续时间马尔可夫链的社交网络变化建模方法，通过少量参数衡量社会结构对变化概率的影响，并给出参数估计方法，帮助理解网络演化。
Abstract This article presents a new methodology for studying a social network of interpersonal relationships, based on stochastic modeling of the changes that occur in the network over time. Specifically, we postulate that these changes can be modeled as a continuous-time Markov chain. The transition rates for the chain are dependent on a small set of parameters that measure the importance of various aspects of social structure on the probability of change. We discuss the assumptions of the framework and describe two simple models that are applications of it. We then present, analyze, and interpret several examples, and we outline methods of parameter estimation. The models prove to be quite effective and allow us to better understand the evolution of a network.