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

Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu Ru Lin, Norbou Buchler

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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.

Original languageEnglish (US)
Article number7762175
Pages (from-to)613-626
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number3
StatePublished - Mar 1 2017


  • Graph kernel
  • Team composition
  • scalability

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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