Missing and spurious interactions and the reconstruction of complex networks
Missing and spurious interactions and the reconstruction of complex networks
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DOI：
10.1073/pnas.0908366106
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发表时间：
2009-12-29
影响因子：
11.1
通讯作者：
Sales-Pardo, Marta
中科院分区：
文献类型：
--
作者：
Guimera, Roger;Sales-Pardo, Marta
目前，网络分析在各种环境中使用，从确定潜在的药物目标到预测流行病的传播和设计疫苗接种策略的传播，从寻找朋友到发现犯罪活动。尽管有网络方法的承诺，但在研究复杂网络的所有领域，网络数据的可靠性还是引起人们关注的根源。在这里，我们提出了一个一般的数学和计算框架，以解决复杂网络中的数据可靠性问题。特别是，我们能够在嘈杂的网络观察中可靠地确定丢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那些嘈杂的观察结果中获得网络重建，从而产生比观测本身提供的更准确的真实网络属性的估计。我们的方法有可能指导实验，更好地表征网络数据集并推动新发现。
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.