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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 social network may be an expert in one or more skills. Edge weights specify 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 individuals of only one skill. We present a 3-approximation algorithm that improves upon a naive extension of the previously known algorithm for densest at least $k$ subgraph problem. We further show how the same approximation can be extended to a special case of multiple skills. Our problem generalizes the formulation studied by Lappas et al. [KDD '09] who measure team compatibility in terms of diameter or spanning tree costs. Experiments are performed on a crawl of the DBLP graph where individuals can be skilled in at most four areas - theory, databases, data mining, and artificial intelligence. In addition to our main algorithm, we also present heuristic extensions to trade off between the size of the solution and its induced density. These density-based algorithms outperform the diameter-based objective on several metrics for assessing the collaborative compatibility of teams. The solutions suggested are also intuitively meaningful and scale well with the increase in the number of skilled individuals required.