Abstract
Given a project requiring a set of skills, the team formation problem in social networks aims to find a team that can cover all the required skills and has the minimal communication cost. Previous studies considered the team formation problem with a leader and proposed efficient algorithms to address the problem. However, for large projects, a single leader is not capable of managing a team with a large number of team members. Thus, a number of leaders would be formed and organized into a hierarchy where each leader is responsible for only a limited number of team members. In this paper, we propose the team formation problem with the communication load constraint in social networks. The communication load constraint limits the number of team members a leader communicates with. To solve the problem, we design a two-phase framework. Based on the proposed framework, we first propose algorithm Opt to find an optimal team, under the communication load constraint, with minimal communication cost. For large social networks, we also propose algorithm Approx to find a nearly-optimal team. Experimental results show that algorithm Opt is able to find optimal teams and is more efficient than the brute-force algorithm. In addition, when nearly-optimal teams are acceptable, algorithm Approx is much more scalable than algorithm Opt for large social networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anagnostopoulos, A., Becchetti, L., Castillo, C., Gionis, A., Leonardi, S.: Power in unity: forming teams in large-scale community systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (2010)
Baykasoglu, A., Dereli, T., Das, S.: Project team selection using fuzzy optimization approach. Cybern. Syst. 38(2), 155–185 (2007)
Chen, S.J., Lin, L.: Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Trans. Eng. Manage. 51(2), 111–124 (2004)
Fitzpatrick, E.L., Askin, R.G.: Forming effective worker teams with multi-functional skill requirements. Comput. Ind. Eng. 48(3), 593–608 (2005)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Co., San Francisco (1979)
Gaston, M.E., Simmons, J., des Jardins, M.: Adapting network structure for efficient team formation. In: Proceedings of the AAAI 2004 Fall Symposium on Artificial Multi-Agent Learning (2004)
Juang, M.C., Huang, C.C., Huang, J.L.: Efficient algorithms for team formation with a leader in social networks. J. Supercomput. 66, 721–737 (2013)
Kargar, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (2011)
Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009)
Singh, S., Srivastava, R., Kumar, V., Agarwal, S.: An approximate algorithm for degree constraint minimum spanning tree. In: International Conference on Computer and Communication Technology (2010)
Zakarian, A., Kusiak, A.: Forming teams: an analytical approach. IIE Trans. 31(1), 85–97 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Teng, YC., Wang, JZ., Huang, JL. (2014). Team Formation with the Communication Load Constraint in Social Networks. In: Peng, WC., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8643. Springer, Cham. https://doi.org/10.1007/978-3-319-13186-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-13186-3_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13185-6
Online ISBN: 978-3-319-13186-3
eBook Packages: Computer ScienceComputer Science (R0)