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.

大多数人类活动都需要正式或非正式团队内部和之间的协作。我们对团队协作努力与绩效的关系的理解仍然存在争议。团队合作形成了一个由潜在重叠组件组成的高度互连的生态系统，其中的任务是在与团队成员和其他团队的交互中执行的。为了解决这个问题，我们提出了一种图神经网络模型来预测团队的表现，同时确定决定此类结果的驱动因素。特别是，该模型基于三个架构渠道：拓扑、中心性和上下文，它们捕获了可能影响团队成功的不同因素。我们赋予模型两种注意力机制，以提高模型性能并允许可解释性。第一个机制允许精确定位团队内部的关键成员。第二种机制使我们能够量化三个驱动效应在确定结果绩效方面的贡献。我们在各个领域测试模型性能，优于大多数经典和神经基线。此外，我们还提供了综合数据集，旨在验证模型如何解开预期属性，在这些属性上我们的模型远远优于基线。