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Recovering lost and absent information in temporal networks
arXiv - CS - Social and Information Networks Pub Date : 2021-07-22 , DOI: arxiv-2107.10835
James P. Bagrow, Sune Lehmann

The full range of activity in a temporal network is captured in its edge activity data -- time series encoding the tie strengths or on-off dynamics of each edge in the network. However, in many practical applications, edge-level data are unavailable, and the network analyses must rely instead on node activity data which aggregates the edge-activity data and thus is less informative. This raises the question: Is it possible to use the static network to recover the richer edge activities from the node activities? Here we show that recovery is possible, often with a surprising degree of accuracy given how much information is lost, and that the recovered data are useful for subsequent network analysis tasks. Recovery is more difficult when network density increases, either topologically or dynamically, but exploiting dynamical and topological sparsity enables effective solutions to the recovery problem. We formally characterize the difficulty of the recovery problem both theoretically and empirically, proving the conditions under which recovery errors can be bounded and showing that, even when these conditions are not met, good quality solutions can still be derived. Effective recovery carries both promise and peril, as it enables deeper scientific study of complex systems but in the context of social systems also raises privacy concerns when social information can be aggregated across multiple data sources.

中文翻译:

恢复时间网络中丢失和缺失的信息

时间网络中的所有活动都在其边缘活动数据中捕获——时间序列对网络中每个边缘的联系强度或开关动态进行编码。然而,在许多实际应用中,边缘级数据不可用,网络分析必须依赖节点活动数据,节点活动数据聚合了边缘活动数据,因此信息量较少。这就提出了一个问题:是否可以使用静态网络从节点活动中恢复更丰富的边缘活动?在这里,我们展示了恢复是可能的,鉴于丢失了多少信息,通常具有惊人的准确度,并且恢复的数据对于后续的网络分析任务很有用。当网络密度增加时,无论是拓扑的还是动态的,恢复都更加困难,但是利用动态和拓扑稀疏性可以有效解决恢复问题。我们从理论上和经验上正式描述了恢复问题的难度,证明了恢复错误可以有界的条件,并表明即使不满足这些条件,仍然可以得出高质量的解决方案。有效的恢复既有希望也有危险,因为它可以对复杂系统进行更深入的科学研究,但在社会系统的背景下,当社会信息可以跨多个数据源聚合时,也会引发隐私问题。即使不满足这些条件,仍然可以得出高质量的解决方案。有效的恢复既有希望也有危险,因为它可以对复杂系统进行更深入的科学研究,但在社会系统的背景下,当社会信息可以跨多个数据源聚合时,也会引发隐私问题。即使不满足这些条件,仍然可以得出高质量的解决方案。有效的恢复既有希望也有危险,因为它可以对复杂系统进行更深入的科学研究,但在社会系统的背景下,当社会信息可以跨多个数据源聚合时,也会引发隐私问题。
更新日期:2021-07-23
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