回归还是探索:模拟网络空间中的人类移动性和动力学

Tianran Hu, Yinglong Xia, Jiebo Luo
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引用次数: 6

摘要

随着许多流行的网络平台广泛采用多社区结构,网络社区间的人员流动越来越受到学术界和工业界的关注。在这项工作中,我们研究了表征网络空间中人类运动的统计模式。受先前关于人类在物理空间中移动的工作的启发,我们将人类的在线活动分解为返回和探索——两种互补的运动类型。然后我们分别研究人们如何表演这两个动作。我们首先提出了一个优先返回模型,揭示了人们返回多个在线社区的优先属性。有趣的是,这个模型与之前关于人类在物理空间中的流动性的发现相呼应。然后,我们提出了一个优先探索模型,从一个新颖的在线社区群体的角度来表征探索运动。我们的实验定量地揭示了人们探索新社区的模式,就基本原则而言,这种模式与在线回归运动有着惊人的相似之处。通过将回归和探索的机制结合起来,我们能够获得一个整体模型,在个人层面上表征网络空间中人类流动模式的特征。我们使用我们的模型进一步调查了人类在线活动,并发现了关于在线社区移动模式的宝贵见解。我们的模型非常好地解释了经验观察到的人类在线运动轨迹,更重要的是,它更好地阐明了对人类网络空间动态的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
To Return or to Explore: Modelling Human Mobility and Dynamics in Cyberspace
With the wide adoption of multi-community structure in many popular online platforms, human mobility across online communities has drawn increasing attention from both academia and industry. In this work, we study the statistical patterns that characterize human movements in cyberspace. Inspired by previous work on human mobility in physical space, we decompose human online activities into return and exploration - two complementary types of movements. We then study how people perform these two movements, respectively. We first propose a preferential return model that uncovers the preferential properties of people returning to multiple online communities. Interestingly, this model echos the previous findings on human mobility in physical space. We then present a preferential exploration model that characterizes exploration movements from a novel online community-group perspective. Our experiments quantitatively reveal the patterns of people exploring new communities, which share striking similarities with online return movements in terms of underlying principles. By combining the mechanisms of both return and exploration together, we are able to obtain an overall model that characterizes human mobility patterns in cyberspace at the individual level. We further investigate human online activities using our models, and discover valuable insights on the mobility patterns across online communities. Our models explain the empirically observed human online movement trajectories remarkably well, and more importantly, sheds better light on the understanding of human cyberspace dynamics.
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