https://doi.org/10.1140/epjds/s13688-023-00442-1
Regular Article
Modeling teams performance using deep representational learning on graphs
1
Computer Science Department, University of Turin, Turin, Italy
2
Bioinformatics Lab, Scuola Normale Superiore, Pisa, Italy
3
Networks and Urban Systems Centre, University of Greenwich, London, UK
4
School of Mathematical Sciences, Queen University of London, London, UK
5
ISI Foundation, Turin, Italy
Received:
23
February
2023
Accepted:
18
December
2023
Published online:
19
January
2024
Most human activities require collaborations within and across formal or informal teams. Our understanding of how the collaborative efforts spent by teams relate to their performance is still a matter of debate. Teamwork results in a highly interconnected ecosystem of potentially overlapping components where tasks are performed in interaction with team members and across other teams. To tackle this problem, we propose a graph neural network model to predict a team’s performance while identifying the drivers determining such outcome. In particular, the model is based on three architectural channels: topological, centrality, and contextual, which capture different factors potentially shaping teams’ success. We endow the model with two attention mechanisms to boost model performance and allow interpretability. A first mechanism allows pinpointing key members inside the team. A second mechanism allows us to quantify the contributions of the three driver effects in determining the outcome performance. We test model performance on various domains, outperforming most classical and neural baselines. Moreover, we include synthetic datasets designed to validate how the model disentangles the intended properties on which our model vastly outperforms baselines.
Key words: Team performance / Graph neural networks / Graph representation learning / Sub-graph classification
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-023-00442-1.
Francesco Carli and Pietro Foini contributed equally to this work.
© The Author(s) 2024
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