Confidence Intervals for Assessing Sizes of Social Network Centralities

Social Networking, 2018, 7, 220-242

23 Pages Posted: 25 Jul 2019

See all articles by Dawn Iacobucci

Dawn Iacobucci

Vanderbilt University - Marketing

Rebecca McBride

Calvin College

Deidre Popovich

Rawls College of Business, Texas Tech University

Maria Rouziou

HEC Paris - Marketing

Date Written: July 24, 2018

Abstract

This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network centrality indices. The first study begins with analyses of stylized networks, which are then perturbed with increasing levels of random noise. When the indices achieve their values for fully random networks, the indices reveal systematic relationships that generalize across network forms. The second study then delves into the relationships between numbers of actors in a network and the density of a network for each of the centrality indices. In doing so, expected values are easily calculated, which in turn enable chi-square tests of network structure. Furthermore, confidence intervals are developed to facilitate a network analyst’s understanding as to which patterns in the data are merely random, versus which are structurally significantly distinct.

Keywords: Centrality, Degree, Closeness, Betweenness, Eigenvector Centrality, Social Networks

Suggested Citation

Iacobucci, Dawn and McBride, Rebecca and Popovich, Deidre and Rouziou, Maria, Confidence Intervals for Assessing Sizes of Social Network Centralities (July 24, 2018). Social Networking, 2018, 7, 220-242, Available at SSRN: https://ssrn.com/abstract=3425950 or http://dx.doi.org/10.2139/ssrn.3425950

Dawn Iacobucci (Contact Author)

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

Rebecca McBride

Calvin College ( email )

Grand Rapids, MI 49546
United States

Deidre Popovich

Rawls College of Business, Texas Tech University ( email )

Lubbock, TX 79409
United States

Maria Rouziou

HEC Paris - Marketing ( email )

Paris
France

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