  The large majority of 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 designed to predict a team's performance while identifying
the drivers that determine such an 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 a wide range of domains outperforming most of the classical and
neural baselines considered. Moreover, we include synthetic datasets
specifically designed to validate how the model disentangles the intended
properties on which our model vastly outperforms baselines.
