Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.
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DOI：
10.1371/journal.pone.0086028
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Cattuto C
中科院分区：
文献类型：
--
作者：
Gauvin L;Panisson A;Cattuto C
随着时间网络数据的不断增加，需要对时间网络中介观结构的提取和表征进行更多的研究，并将这种结构与系统的特定功能或特性联系起来。一个突出的挑战是将静态网络的结果扩展到时变网络，在时变网络中，系统的拓扑结构及其组件的时间活动模式是交织在一起的。在这里，我们研究了使用一种潜在因素分解技术，非负张量分解，来提取时间网络的社区活动结构。该方法本质上是暂时的，允许同时识别社区并随时间跟踪其活动。我们将时间网络的时变邻接矩阵表示为一个三向张量，并将这个张量近似为可以解释为具有相关活动时间序列的节点社区的项的总和。我们总结了张量分解的已知计算技术，并讨论了一些可用于调整分解表示复杂性的质量指标。随后，我们将张量分解应用于一个时间网络，其中一个基本真理可用于社区结构和时间活动模式。我们使用的数据描述了学生在学校的社会互动，学生与学校班级之间的联系，以及学生随时间的时空轨迹。我们证明了非负张量分解能够高精度地恢复类结构。特别是，提取的张量分量既可以作为已知的学校班级进行验证，也可以根据相关的活动模式进行验证，即由已知的学校活动时间表确定的空间和时间巧合。
The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.
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DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne
影响因子：
3.7
作者：
Cattuto C;Van den Broeck W;Barrat A;Colizza V;Pinton JF;Vespignani A
通讯作者：
Vespignani A
影响因子：
3.1
作者：
Bader, Brett W.;Kolda, Tamara G.
通讯作者：
Kolda, Tamara G.
影响因子：
64.8
作者：
Lee, DD;Seung, HS
通讯作者：
Seung, HS
影响因子：
2.9
作者：
Bassett, Danielle S.;Porter, Mason A.;Mucha, Peter J.
通讯作者：
Mucha, Peter J.