Department of Chemical and Biological Engineering and Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208, USA.
Proc Natl Acad Sci U S A. 2009 Dec 29;106(52):22073-8. doi: 10.1073/pnas.0908366106. Epub 2009 Dec 14.
Network analysis is currently used in a myriad of contexts, from identifying potential drug targets to predicting the spread of epidemics and designing vaccination strategies and from finding friends to uncovering criminal activity. Despite the promise of the network approach, the reliability of network data is a source of great concern in all fields where complex networks are studied. Here, we present a general mathematical and computational framework to deal with the problem of data reliability in complex networks. In particular, we are able to reliably identify both missing and spurious interactions in noisy network observations. Remarkably, our approach also enables us to obtain, from those noisy observations, network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves. Our approach has the potential to guide experiments, to better characterize network data sets, and to drive new discoveries.
网络分析目前被应用于许多领域，从识别潜在的药物靶点到预测传染病的传播和设计疫苗接种策略，从寻找朋友到发现犯罪活动。尽管网络方法有很大的前景，但在研究复杂网络的所有领域中，网络数据的可靠性都是一个令人担忧的问题。在这里，我们提出了一个通用的数学和计算框架来处理复杂网络中数据可靠性的问题。特别是，我们能够可靠地识别噪声网络观测中缺失和虚假的相互作用。值得注意的是，我们的方法还使我们能够从这些噪声观测中获得网络重建，从而获得比观测本身提供的更准确的真实网络特性的估计。我们的方法有可能指导实验，更好地描述网络数据集，并推动新的发现。
