Permutation and randomization tests for network analysis

Mark M. Fredrickson, Yuguo Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Permutation tests have a long history in testing hypotheses of independence between nodal attributes and network structure, though they are often thought less informative than parametric modeling techniques. In this paper, we show that when the nodal attribute is random assignment to a treatment condition, permutation tests provide a valid test of the causal effect of treatment. We discuss existing test statistics used in network permutation tests and propose several new statistics. In simulations we find that these statistics perform well compared to parametric tests and that specific statistics can be selected to provide power against common network models. We illustrate the methods with gene-wide association study performed on randomized study participants and an observational study of gender membership on Scandinavian corporate boards.

Original languageEnglish (US)
Pages (from-to)171-183
Number of pages13
JournalSocial Networks
Volume59
DOIs
StatePublished - Oct 2019

Keywords

  • Causal inference
  • Centrality
  • Clustering
  • Coefficient of determination
  • Edge counts
  • Hypothesis testing
  • Mahalanobis distance
  • Permutation
  • Quadratic assignment procedure (QAP)
  • Randomization

ASJC Scopus subject areas

  • Anthropology
  • Sociology and Political Science
  • Social Sciences(all)
  • Psychology(all)

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