论文标题
高阶不断发展的网络的时间优势特性
Temporal-topological properties of higher-order evolving networks
论文作者
论文摘要
人类的社交互动通常被记录为特定时间的二元相互作用,并表示为不断发展的(时间)网络,其中链接随着时间而被激活/停用。但是,个人可以分为两个以上的人互动。这样的组互动可以表示为不断发展的网络的高阶事件。在这里,我们提出的方法是表征高阶事件的时间题学特性,以比较网络并确定其(DIS)相似性。我们分析了8个现实世界的物理接触网络,发现以下内容:a)随着时间的流逝,不同订单的事件往往也很接近拓扑; b)参与给定顺序的许多不同组(事件)的节点往往参与其他顺序的许多不同组(事件);因此,个人在跨阶的事件中往往始终保持活跃或不活跃。 c)拓扑结束的本地事件随时间相关,支持观察a)。不同的是,在5个协作网络中,观察a)几乎没有;一致地,在协作网络中未观察到本地事件的时间相关性。与协作网络相比,两类网络之间的这种差异可以通过以下事实来解释。我们的方法可能有助于研究高阶事件的性质如何影响在它们上展开的动态过程,并可能激发更精致的高阶时间变化网络模型的发展。
Human social interactions are typically recorded as time-specific dyadic interactions, and represented as evolving (temporal) networks, where links are activated/deactivated over time. However, individuals can interact in groups of more than two people. Such group interactions can be represented as higher-order events of an evolving network. Here, we propose methods to characterize the temporal-topological properties of higher-order events to compare networks and identify their (dis)similarities. We analyzed 8 real-world physical contact networks, finding the following: a) Events of different orders close in time tend to be also close in topology; b) Nodes participating in many different groups (events) of a given order tend to involve in many different groups (events) of another order; Thus, individuals tend to be consistently active or inactive in events across orders; c) Local events that are close in topology are correlated in time, supporting observation a). Differently, in 5 collaboration networks, observation a) is almost absent; Consistently, no evident temporal correlation of local events has been observed in collaboration networks. Such differences between the two classes of networks may be explained by the fact that physical contacts are proximity based, in contrast to collaboration networks. Our methods may facilitate the investigation of how properties of higher-order events affect dynamic processes unfolding on them and possibly inspire the development of more refined models of higher-order time-varying networks.