Issue No. 03 - March (2017 vol. 29)
Liangyue Li , School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ
Hanghang Tong , School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, AZ
Nan Cao , Tongji University, 281 Fuxin Road, Shanghai, China
Kate Ehrlich , IBM Research Cambridg, One Rogers St, Cambridge MA
Yu-Ru Lin , School of Information Sciences, University of Pittsburgh, Pittsburgh, PA
Norbou Buchler , US Army Research Laboratory, Adelphi, MD
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.
Kernel, Algorithm design and analysis, Social network services, Indexes, Electronic mail, Natural language processing
L. Li, H. Tong, N. Cao, K. Ehrlich, Y. Lin and N. Buchler, "Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 3, pp. 613-626, 2017.