Multi-skill Collaborative Teams based on Densest Subgraphs

We consider the problem of identifying a team of skilled individuals for collaboration, in the presence of a social network. Each node in the input social network may be an expert in one or more skills - such as theory, databases or data mining. The edge weights specify the affinity or collaborative compatibility between respective nodes. Given a project that requires a set of specified number of skilled individuals in each area of expertise, the goal is to identify a team that maximizes the collaborative compatibility. For example, the requirement may be to form a team that has at least three databases experts and at least two theory experts. We explore team formation where the collaborative compatibility objective is measured as the density of the induced subgraph on selected nodes. The problem of maximizing density is NP-hard even when the team requires a certain number of individuals of only one specific skill. We present a 3-approximation algorithm that improves upon a naive ex...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Amita Gajewar, Atish Das Sarma
• Keywords: Collaborative Compatibility, CORR 2011, Education, Skilled Individuals, Teams