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

Brennan Antone; Aryaman Gupta; Suzanne Bell; Leslie DeChurch; Noshir Contractor

doi:10.1007/978-3-030-36683-4_81

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 proceeding › Conference contribution

5 Scopus citations

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 language	English (US)
Title of host publication	Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
Editors	Hocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Publisher	Springer
Pages	1018-1030
Number of pages	13
ISBN (Print)	9783030366827
DOIs	https://doi.org/10.1007/978-3-030-36683-4_81
State	Published - 2020
Event	8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019 - Lisbon, Portugal Duration: Dec 10 2019 → Dec 12 2019

Publication series

Name	Studies in Computational Intelligence
Volume	882 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019
Country/Territory	Portugal
City	Lisbon
Period	12/10/19 → 12/12/19

Funding

This material is based upon work supported by NASA under award numbers NNX15AM32G, NNX15AM26G, and 80NSSC18K0221. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.

Keywords

Network properties
Separable temporal exponential random graph models
Team performance

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1007/978-3-030-36683-4_81

Cite this

Antone, B., Gupta, A., Bell, S., DeChurch, L., & Contractor, N. (2020). Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019 (pp. 1018-1030). (Studies in Computational Intelligence; Vol. 882 SCI). Springer. https://doi.org/10.1007/978-3-030-36683-4_81

Antone, Brennan ; Gupta, Aryaman ; Bell, Suzanne et al. / Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls. Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. editor / Hocine Cherifi ; Sabrina Gaito ; José Fernendo Mendes ; Esteban Moro ; Luis Mateus Rocha. Springer, 2020. pp. 1018-1030 (Studies in Computational Intelligence).

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title = "Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls",

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.",

keywords = "Network properties, Separable temporal exponential random graph models, Team performance",

author = "Brennan Antone and Aryaman Gupta and Suzanne Bell and Leslie DeChurch and Noshir Contractor",

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year = "2020",

doi = "10.1007/978-3-030-36683-4_81",

language = "English (US)",

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Antone, B, Gupta, A, Bell, S, DeChurch, L & Contractor, N 2020, Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls. in H Cherifi, S Gaito, JF Mendes, E Moro & LM Rocha (eds), Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol. 882 SCI, Springer, pp. 1018-1030, 8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019, Lisbon, Portugal, 12/10/19. https://doi.org/10.1007/978-3-030-36683-4_81

Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls. / Antone, Brennan; Gupta, Aryaman; Bell, Suzanne et al.
Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. ed. / Hocine Cherifi; Sabrina Gaito; José Fernendo Mendes; Esteban Moro; Luis Mateus Rocha. Springer, 2020. p. 1018-1030 (Studies in Computational Intelligence; Vol. 882 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - DeChurch, Leslie

AU - Contractor, Noshir

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N2 - 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.

AB - 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.

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ER -

Antone B, Gupta A, Bell S, DeChurch L , Contractor N. Testing Influence of Network Structure on Team Performance Using STERGM-Based Controls. In Cherifi H, Gaito S, Mendes JF, Moro E, Rocha LM, editors, Complex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. Springer. 2020. p. 1018-1030. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-36683-4_81

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

Abstract

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