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) |
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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 | |
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 |
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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 |
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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