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

Li, L and Tong, H and Cao, N and Ehrlich, K and Lin, YR and Buchler, N (2017) Enhancing Team Composition in Professional Networks: Problem Definitions and Fast Solutions. IEEE Transactions on Knowledge and Data Engineering, 29 (3). 613 - 626. ISSN 1041-4347

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Abstract

© 2016 IEEE. 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Li, L
Tong, H
Cao, N
Ehrlich, K
Lin, YRyurulin@pitt.eduYURULIN
Buchler, N
Date: 1 March 2017
Date Type: Publication
Journal or Publication Title: IEEE Transactions on Knowledge and Data Engineering
Volume: 29
Number: 3
Page Range: 613 - 626
DOI or Unique Handle: 10.1109/tkde.2016.2633464
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1041-4347
Date Deposited: 26 Jun 2017 16:28
Last Modified: 13 Oct 2017 20:57
URI: http://d-scholarship.pitt.edu/id/eprint/32554

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