Faculty of Electrical Engineering, Mathematics, and Computer Science, Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands.
Sci Rep. 2023 Apr 11;13(1):5885. doi: 10.1038/s41598-023-32253-9.
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.
人类的社会互动通常被记录为特定时间的二元互动,并表示为随时间演变的(时间)网络,其中链接随时间激活/去激活。然而,个体可以在超过两个人的群体中进行互动。这种群体互动可以表示为随时间演变的网络的高阶事件。在这里,我们提出了一些方法来描述高阶事件的时间拓扑性质,以比较网络并识别它们的(不)相似性。我们分析了 8 个真实世界的物理接触网络,发现:(a) 时间上接近的不同阶事件在拓扑上也往往接近;(b) 参与给定阶数的许多不同群体(事件)的节点往往会参与另一个阶数的许多不同群体(事件);因此,个体在不同阶数的事件中往往保持活跃或不活跃;(c) 在拓扑上接近的局部事件在时间上是相关的,这支持了观察 (a)。相比之下,在 5 个合作网络中,观察 (a) 几乎不存在;同样,在合作网络中也没有观察到局部事件的明显时间相关性。这两类网络之间的差异可能是由于物理接触是基于接近度的,而与合作网络不同。我们的方法可能有助于研究高阶事件的属性如何影响在它们上展开的动态过程,并可能启发更精细的高阶时变网络模型的发展。