Estimating network structure from unreliable measurements

2018/03/31 by M. E. J. Newman
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computer science #Gene Regulatory Network Analysis #cs.SI #physics.soc-ph

paper · pdf · doi:10.1103/physreve.98.062321

published as Phys. Rev. E 98, 062321 (2018) · 19 pages, 3 figures. Title changed in this version. Other minor updates and corrections

arxiv created 2018/12/18 · openalex publication_date 2018/12/26 · arxiv updated 2019/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

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.

Citations

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