Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls

Brennan Antone*, Aryaman Gupta, Suzanne Bell, Leslie DeChurch, Noshir Contractor

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We demonstrate an approach to perform significance testing on the association between two different network-level properties, based on the observation of multiple networks over time. This approach may be applied, for instance, to evaluate how patterns of social relationships within teams are associated with team performance on different tasks. We apply this approach to understand the team processes of crews in long-duration space exploration analogs. Using data collected from crews in NASA analogs, we identify how interpersonal network patterns among crew members relate to performance on various tasks. In our significance testing, we control for complex interdependencies between network ties: structural patterns, such as reciprocity, and temporal patterns in how ties tend to form or dissolve over time. To accomplish this, Separable Temporal Exponential Random Graph Models (STERGMs) are used as a parametric approach for sampling from the null distribution, in order to calculate p-values.

Original languageEnglish (US)
Title of host publicationComplex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
EditorsHocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
PublisherSpringer
Pages1018-1030
Number of pages13
ISBN (Print)9783030366827
DOIs
StatePublished - 2020
Event8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019 - Lisbon, Portugal
Duration: Dec 10 2019Dec 12 2019

Publication series

NameStudies in Computational Intelligence
Volume882 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
CountryPortugal
CityLisbon
Period12/10/1912/12/19

Keywords

  • Network properties
  • Separable temporal exponential random graph models
  • Team performance

ASJC Scopus subject areas

  • Artificial Intelligence

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