Enhancing Team Composition in Professional Networks Problem Definitions and Fast Solutions
Abstract: In this paper, we study ways to enhance the composition of teams based on new requirements in a collaborative environment. We focus on recommending team members who can maintain the team's performance by minimizing changes to the team's skills and social structure. Our recommendations are based on computing team-level level similarity, which includes skill similarity, structural similarity as well as the synergyy between the two. Current heuristic approaches are oneone dimensional and not comprehensive, as they consider the two aspects independently. To formalize team team-level level similarity, we adopt the notion of graph kernel of attributed graphs to encompass the two asp aspects ects and their interaction. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. Extensive empirical evaluations on real world datasets validate the effectiveness and efficiency of our algorithms.