Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions

• Published in: IEEE transactions on knowledge and data engineering Vol. 29; no. 3; pp. 613 - 626
• Main Authors: Li, Liangyue, Tong, Hanghang, Cao, Nan, Ehrlich, Kate, Lin, Yu-Ru, Buchler, Norbou
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Electronic mail; Graph kernel; Indexes; Kernel; Natural language processing; Pruning; scalability; Similarity; Smoothness; Social network services; team composition; graph kernel; scalability; Team composition
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2016.2633464

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 similarity, which includes skill similarity, structural similarity as well as the synergy between the two. Current heuristic approaches are one-dimensional and not comprehensive, as they consider the two aspects independently. To formalize team-level similarity, we adopt the notion of graph kernel of attributed graphs to encompass the two aspects 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.