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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Missing and spurious interactions and the reconstruction of complex networks
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Missing and spurious interactions and the reconstruction of complex networks

机译：缺失和虚假的交互以及复杂网络的重建

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摘要

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.
机译：网络分析目前用于各种各样的环境中，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，从寻找朋友到发现犯罪活动。尽管有使用网络方法的希望，但是在研究复杂网络的所有领域中，网络数据的可靠性仍然是引起人们高度关注的问题。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的数据可靠性问题。特别是，我们能够在嘈杂的网络观测中可靠地识别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那些嘈杂的观察中获得网络重构，该重构可以得出比观察本身提供的更准确的真实网络属性估计。我们的方法具有指导实验，更好地表征网络数据集和推动新发现的潜力。

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