Predicting future conflict between team-members with parameter-free models of social networks

Abstract

Despite the well-documented benefits of working in teams, teamwork also results in communication, coordination and management costs, and may lead to personal conflict between team members. In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using parameter-free models of social network structure. We analyze data of conflict appearance and resolution between 86 team members in 16 small teams, all working in a real project for nine consecutive months. We find that group-based models of complex networks successfully anticipate conflict in small teams whereas micro-based models of structural balance, which have been traditionally used to model conflict, do not.

References in corpus (5)

• Statistical mechanics of complex networks
• The structure and function of complex networks
• Missing and spurious interactions and the reconstruction of complex networks
• Analysis of a continuous-time model of structural balance
• Predicting human preferences using the block structure of complex social networks

Cited by in corpus (11)

• Efficiently inferring community structure in bipartite networks
• Inferring the mesoscale structure of layered, edge-valued and time-varying networks
• Bayesian stochastic blockmodeling
• Multilayer stochastic block models reveal the multilayer structure of complex networks
• Matching-centrality decomposition and the forecasting of new links in networks
• Review on Learning and Extracting Graph Features for Link Prediction
• Network structure, metadata and the prediction of missing nodes and annotations
• Tensorial and bipartite block models for link prediction in layered networks and temporal networks
• Leadership Network and Team Performance in Interactive Contests
• Algorithmic detectability threshold of the stochastic block model
• Network-based models for social recommender systems