论文标题
基于Copula的简单时间网络的自相关功能的分析
Copula-based analysis of the autocorrelation function for simple temporal networks
论文作者
论文摘要
为了表征时间网络中的时间相关性，我们根据两个通过特定时间间隔隔开的快照网络之间的相似性来定义时间网络的自相关函数（ACF）。通过采用最近为单个时间序列开发的基于COPULA的方法，我们分析了时间网络的ACF，其中链接的活动模式彼此独立，但它们的活动水平是异质的。通过假设指数分布的间隔时间在每个链接中彼此弱相关，我们获得了ACF的分析解决方案。分析解决方案的有效性针对数值模拟进行了测试，以发现数值结果与分析溶液相当。
To characterize temporal correlations in temporal networks, we define an autocorrelation function (ACF) for temporal networks in terms of the similarity between two snapshot networks separated by a certain time interval. By employing a copula-based method recently developed for a single time series, we analyze the ACF for the temporal network in which activity patterns of links are independent of each other but their activity levels are heterogeneous. By assuming that exponential distributed interevent times are weakly correlated with each other in each link, we obtain an analytical solution of the ACF. The validity of the analytical solution is tested against the numerical simulations to find that the numerical results are comparable to the analytical solution.
