Hulovatyy Y, Chen H, Milenković T
Department of Computer Science and Engineering, Interdisciplinary Center for Network Science and Applications, and ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA.
Bioinformatics. 2015 Jun 15;31(12):i171-80. doi: 10.1093/bioinformatics/btv227.
With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships.
We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging.
随着时间性现实世界网络的可用性不断增加，如何有效地研究这些数据？人们可以将时间性网络建模为单个聚合静态网络，或者建模为一系列特定时间的快照，每个快照都是对应时间窗口上的聚合静态网络。然后，可以对得到的聚合网络使用既定的静态分析方法，但在此过程中会分别完全丢失或在不同快照之间的接口处丢失有价值的时间信息。在这里，我们通过捕获快照间关系开发了一种更明确地研究时间性网络的新方法。
我们的方法基于已确立的图元（子图），其在静态网络研究的众多背景下已得到验证。我们开发了新理论以允许对时间性网络进行基于图元的分析。我们的动态图元新概念不同于现有的基于时间性模体（具有统计显著性子图）的动态网络方法。后者存在局限性：其结果取决于用于评估子图显著性所需的空网络模型的选择，而选择一个好的空模型并非易事。我们的动态图元克服了时间性模体的局限性。此外，当我们旨在刻画整个时间性网络或单个节点的结构和功能时，我们的动态图元优于静态图元。显然，考虑时间信息是有帮助的。我们将动态图元应用于特定年龄的时间性分子网络数据，以加深我们对人类衰老的有限认识。
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