论文标题
面对面人类互动的时间网络中的因果路径
Causal Paths in Temporal Networks of Face-to-Face Human Interactions
论文作者
论文摘要
在时间网络中，因果路径的特征是从源到目标的链接必须尊重时间顺序。在本文中，我们研究了人脸的时间网络中的因果路径结构，以面对不同社会背景下的相互作用。在静态网络路径中，即传递链接，从$ a $到$ b $，从$ b $到$ c $的链接意味着从$ a $ a $到$ c $ a $ b $的路径存在。在时间网络中，按时间顺序的约束引入了影响传播的时间相关性。基于高阶马尔可夫连锁店的概率模型表明，只有当连续事件之间的时间间隙大于平均值，并且在此值以下可忽略不计时，可能会导致传递性无效的相关性。时间和静态可访问性矩阵的密度之间的比较表明，静态表示可以很好地使用。此外，我们随着时间的推移量化了网络的因果关系区域的程度。
In a temporal network causal paths are characterized by the fact that links from a source to a target must respect the chronological order. In this article we study the causal paths structure in temporal networks of human face to face interactions in different social contexts. In a static network paths are transitive i.e. the existence of a link from $a$ to $b$ and from $b$ to $c$ implies the existence of a path from $a$ to $c$ via $b$. In a temporal network the chronological constraint introduces time correlations that affects transitivity. A probabilistic model based on higher order Markov chains shows that correlations that can invalidate transitivity are present only when the time gap between consecutive events is larger than the average value and are negligible below such a value. The comparison between the densities of the temporal and static accessibility matrices shows that the static representation can be used with good approximation. Moreover, we quantify the extent of the causally connected region of the networks over time.
