Estimating network structure from unreliable measurements
Estimating network structure from unreliable measurements
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DOI：
10.1103/physreve.98.062321
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发表时间：
2018-03
影响因子：
2.4
通讯作者：
M. Newman
中科院分区：
文献类型：
--
作者：
M. Newman
大多数网络的经验研究都假设我们给出的网络数据代表了感兴趣系统中节点和边的完整和准确的图像，但在现实世界中，情况很少如此。更常见的情况是，这些数据只是不完美地描述了网络结构--就像基本上所有其他经验科学领域的数据一样，网络数据很容易出现测量误差和噪声。同时，数据可能比简单的网络测量更丰富，包含多个测量、权重、边的长度或强度、节点或边标签或各种注释。在这里，我们开发了一个通用的方法，使用任何形式的网络数据，简单或复杂的，当数据是不可靠的，网络结构和属性的估计，并给出了示例应用程序的选择社会和生物网络。
Most empirical studies of networks assume that the network data we are given represent a complete and accurate picture of the nodes and edges in the system of interest, but in real-world situations this is rarely the case. More often the data only specify the network structure imperfectly -- like data in essentially every other area of empirical science, network data are prone to measurement error and noise. At the same time, the data may be richer than simple network measurements, incorporating multiple measurements, weights, lengths or strengths of edges, node or edge labels, or annotations of various kinds. Here we develop a general method for making estimates of network structure and properties using any form of network data, simple or complex, when the data are unreliable, and give example applications to a selection of social and biological networks.