Network structure from rich but noisy data
Network structure from rich but noisy data
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DOI：
10.1038/s41567-018-0076-1
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发表时间：
2018-06-01
期刊：
影响因子：
19.6
通讯作者：
Newman, M. E. J.
中科院分区：
文献类型：
--
作者：
Newman, M. E. J.
在科学界日益增长的兴趣的推动下，近年来，人们对从互联网和万维网到生物网络和社交网络的网络结构进行了大量的实证研究。这些实验产生的数据通常是丰富的、多模式的，但同时它们可能包含很大的测量误差(1-7)。对网络系统的准确分析和理解需要一种从如此丰富但嘈杂的数据中估计网络真实结构的方法(8-15)。在这里，我们描述了一种技术，使我们能够从任意格式的复杂数据中对网络结构进行最佳估计，包括可能存在许多不同类型的测量、重复观察、矛盾观察、注释或元数据或丢失数据的情况。我们给出了两种不同社交网络的示例应用，一种来自面对面的互动，另一种来自自我报告的友谊。
Driven by growing interest across the sciences, a large number of empirical studies have been conducted in recent years of the structure of networks ranging from the Internet and the World Wide Web to biological networks and social networks. The data produced by these experiments are often rich and multimodal, yet at the same time they may contain substantial measurement error(1-7). Accurate analysis and understanding of networked systems requires a way of estimating the true structure of networks from such rich but noisy data(8-15). Here we describe a technique that allows us to make optimal estimates of network structure from complex data in arbitrary formats, including cases where there may be measurements of many different types, repeated observations, contradictory observations, annotations or metadata, or missing data. We give example applications to two different social networks, one derived from face-to-face interactions and one from self-reported friendships.