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Mark Newman

Print publication date: 2018

Print ISBN-13: 9780198805090

Published to Oxford Scholarship Online: October 2018

DOI: 10.1093/oso/9780198805090.001.0001

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Network statistics and measurement error

Network statistics and measurement error

(p.275) Chapter 9 Network statistics and measurement error

Mark Newman

Oxford University Press

This chapter introduces the mathematics of network statistics, the quantification of errors in network data, and the estimation of network structure in the presence of error. The discussion starts with a summary of the types of error that can occur in network data and the empirical sources of those errors. The remainder of the chapter is given over to a discussion of the theory of network statistics, beginning with a review of the theory for ordinary real-valued (non-network) data, then developing the expectation-maximization (EM) algorithm for estimating network structure and error levels in the presence of error, with example applications. The chapter ends with a discussion of error correction methods such as link prediction and node disambiguation.

Keywords:   Statistics, measurement error, missing data, aggregation, disaggregation, error model, maximum likelihood, expectation-maximization algorithm, EM algorithm, false positive, false negative, error correction, link prediction, node disaggregation

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