Abstract
Given a task T, a pool of experts χ with different skills, and a social network G that captures social relationships and various interactions among these experts, we study the problem of finding a wise group of experts χ', a subset of χ, to perform the task. We call this the Expert Group Formation problem in this paper. In order to reduce various potential social influence among team members and avoid following the crowd, we require that the members of χ' not only meet the skill requirements of the task, but also be diverse. To quantify the diversity of a group of experts, we propose one metric based on the social influence incurred by the subgraph in G that only involves χ'. We analyze the problem of Diverse Expert Group Formation and show that it is NP-hard. We explore its connections with existing combinatorial problems and propose novel algorithms for its approximation solution. To the best of our knowledge, this is the first work to study diversity in the social graph and facilitate its effect in the Expert Group Formation problem. We conduct extensive experiments on the DBLP dataset and the experimental results show that our framework works well in practice and gives useful and intuitive results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aleman-Meza, B., Nagarajan, M., Ramakrishnan, C., Ding, L., Kolari, P., Sheth, A.P., Arpinar, I.B., Joshi, A., Finin, T.: Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection. In: WWW 2006, pp. 407–416 (2006)
Arkin, E.M., Hassin, R.: Minimum-diameter covering problems. Networks 36, 147–155 (2000)
Baykasoglu, A., Dereli, T., Das, S.: Project team selection using fuzzy optimization approach. Cybern. Syst. 38, 155–185 (2007)
David Easley, J.K.: Networks, Crowds, and Markets Reasoning About a Highly Connected World. Cambridge University (2010)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: WSDM 2010, pp. 241–250 (2010)
Chen, S.j., Lin, L.: Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering (2004)
Karimzadehgan, M., Zhai, C., Belford, G.: Multi-aspect expertise matching for review assignment. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, pp. 1113–1122 (2008)
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, pp. 467–476 (2009)
Gaston, J.M., des Jardins, M.: Adapting network structures for efficient team formation. In: Proceedings of the AAAI Fall Symposium on Artificial Multi-Agent Learning (2004)
Newman, M.E.J.: Scientific collaboration networks. i. network construction and fundamental results. Rev. E 64 (2001)
Newman, M.E.J.: Scientific collaboration networks. ii. shortest paths, weighted networks, and centrality. Physical Review E 64(1), 016132+ (2001)
Newman, M.E.J.: Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 5200–5205 (2004)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD 2009, pp. 807–816 (2009)
Wi, H., Oh, S., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl., 9121–9134 (July 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yin, H., Cui, B., Huang, Y. (2011). Finding a Wise Group of Experts in Social Networks. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-25853-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25852-7
Online ISBN: 978-3-642-25853-4
eBook Packages: Computer ScienceComputer Science (R0)