Extracting a proper dynamic network for modeling a time-dependent complex system is an important issue. Building a correct model is related to finding out critical time points where a system exhibits considerable change. In this work, we propose to measure network similarity to detect proper time intervals. We develop three similarity metrics, node, link, and neighborhood similarities, for any consecutive snapshots of a dynamic network. Rather than a label or a user-defined threshold, we use statistically expected values of proposed similarities under a null-model to state whether the system changes critically. We experimented on two different data sets with different temporal dynamics: the Wi-Fi access points logs of a university campus and Enron emails. Results show that, first, proposed similarities reflect similar signal trends with network topological properties with less noisy signals, and their scores are scale invariant. Second, proposed similarities generate better signals than adjacency correlation with optimal noise and diversity. Third, using statistically expected values allows us to find different time intervals for a system, leading to the extraction of non-redundant snapshots for dynamic network modeling.

提取合适的动态网络以对依赖于时间的复杂系统进行建模是一个重要的问题。建立正确的模型与找出系统表现出显着变化的关键时间点有关。在这项工作中，我们建议测量网络相似性以检测适当的时间间隔。我们为动态网络的任何连续快照开发了三个相似性度量，节点、链接和邻域相似性。我们不是使用标签或用户定义的阈值，而是使用空模型下提议的相似性的统计预期值来说明系统是否发生了严重变化。我们在两个具有不同时间动态的不同数据集上进行了实验：大学校园的 Wi-Fi 接入点日志和安然电子邮件。结果表明，首先，提出的相似性反映了具有网络拓扑特性的相似信号趋势，具有较少的噪声信号，并且它们的分数是尺度不变的。其次，提出的相似性比具有最佳噪声和多样性的邻接相关产生更好的信号。第三，使用统计期望值使我们能够找到系统的不同时间间隔，从而为动态网络建模提取非冗余快照。