Finding proper time intervals for dynamic network extraction
Finding proper time intervals for dynamic network extraction
复制标题
寻找动态网络提取的适当时间间隔
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
H. Boz
中科院分区：
文献类型：
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作者：
Günce Keziban Orman;Nadir Türe;Selim Balcisoy;H. Boz
提取一个合适的动态网络来建模时变复杂系统是一个重要的问题。建立一个正确的模型与找出系统表现出相当大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来检测适当的时间间隔。我们开发了三个相似性度量，节点，链接和邻域相似性，任何连续的动态网络的快照。而不是一个标签或用户定义的阈值，我们使用统计预期值的建议的相似性下的空模型来说明系统是否发生了重大变化。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，第一，建议的相似性反映了相似的信号趋势与网络拓扑特性与噪声较少的信号，其分数是尺度不变的。其次，建议的相似性产生更好的信号比相邻相关与最佳的噪声和多样性。第三，使用统计预期值允许我们找到系统的不同时间间隔，从而提取用于动态网络建模的非冗余快照。
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.
影响因子：
3.7
作者：
Peter Wills;François G. Meyer
通讯作者：
Peter Wills;François G. Meyer