Advertisement

Knowledge and Information Systems

, Volume 42, Issue 2, pp 441–463 | Cite as

On team formation with expertise query in collaborative social networks

  • Cheng-Te LiEmail author
  • Man-Kwan Shan
  • Shou-De Lin
Regular Paper

Abstract

Given a collaborative social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfies the requirements of the given task but also is able to communicate with one another in an effective manner. This paper extends the original team formation problem to a generalized version, in which the number of experts selected for each required skill is also specified. The constructed teams need to contain adequate number of experts for each required skill. We develop two approaches to compose teams for the proposed generalized team formation tasks. First, we consider the specific number of experts to devise the generalized Enhanced-Steiner algorithm. Second, we present a grouping-based method condensing the expertise information to a compact representation, group graph, based on the required skills. Group graph can not only reduce the search space but also eliminate redundant communication cost and filter out irrelevant individuals when compiling team members. To further improve the effectiveness of the composed teams, we propose a density-based measure and embed it into the developed methods. Experimental results on the DBLP network show that the teams composed by the proposed methods have better performance in both effectiveness and efficiency.

Keywords

Team formation Social network Expertise query  Collaborative networks 

References

  1. 1.
    Agustín-Blas LE, Salcedo-Sanz S, Ortiz-García EG, Portilla-Figueras A, Pérez-Bellido AM, Jiménez-Fernández S (2010) Team formation based on group technology: a hybrid grouping genetic algorithm approach. Comput Oper Res 38:484–495CrossRefGoogle Scholar
  2. 2.
    Anagnostopoulos A, Becchetti L, Castillo C, Gionis A, Leonardi S (2010) Power in unity: forming teams in large-scale community systems. In: Proceedings of ACM international conference on information and knowledge management (CIKM’10), pp 599–608Google Scholar
  3. 3.
    Anagnostopoulos A, Becchetti L, Castillo C, Gionis A, Leonardi S (2012) Online team formation in social networks. In: Proceedings of ACM international conference on World Wide Web (WWW’12), pp 839–848Google Scholar
  4. 4.
    Cheatham M, Cleereman K (2006) Application of social network analysis to collaborative team formation. In: Proceedings of international symposium on collaborative technologies and systems, pp 306–311Google Scholar
  5. 5.
    Chen SJ, Lin L (2004) Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering. IEEE Trans Eng Manag 51(2):111–124CrossRefGoogle Scholar
  6. 6.
    Cheng J, Ke Y, Ng W (2009a) Efficient processing of group-oriented connection queries in a large graph. In: Proceedings of ACM international conference on information and knowledge management (CIKM’09), pp 1481–1484Google Scholar
  7. 7.
    Cheng J, Ke Y, Ng W, Yu JX (2009b) Context-aware object connection discovery in large graphs. In: Proceedings of IEEE international conference on data engineering (ICDE’09), pp 856–867Google Scholar
  8. 8.
    Faloutsos C, McCurley KS, Tomkins A (2004) Fast discovery of connection subgraph. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’04), pp 118–127Google Scholar
  9. 9.
    Fitzpatrick EL, Askin RG (2005) Forming effective worker teams with multi-functional skill requirements. Comput Ind Eng 48(3):593–608CrossRefGoogle Scholar
  10. 10.
    Gaston M, Simmons J, DesJardins M (2004) Adapting network structures for efficient team formation. In: Proceedings of the AAAI fall symposium on artificial multi-agent learningGoogle Scholar
  11. 11.
    Kargar M, An A (2011) Discovering top-k teams of experts with/without a leader in social networks. In: Proceedings of ACM international conference on information and knowledge management (CIKM’11), pp 985–994Google Scholar
  12. 12.
    Kargar M, An A, Zihayat M (2012) Efficient bi-objective team formation in social networks. Mach Learn Knowl Discov Databases 7524:483–498, LNCSGoogle Scholar
  13. 13.
    Kasneci G, Ramanath M, Sozio M, Suchanek FM, Weikum G (2009) STAR: Steiner-tree approximation in relationship graphs. In: Proceedings of IEEE international conference on data engineering (ICDE’09), pp 868–879Google Scholar
  14. 14.
    Lappas T, Liu K, Terzi E (2009) Finding a team of experts in social networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’09), pp 467–475Google Scholar
  15. 15.
    Li C-T, Shan M-K, Lin S-D (2011) Context-based people search in labeled social networks. In: Proceedings of ACM international conference on information and knowledge management (CIKM’11), pp 1607–1612Google Scholar
  16. 16.
    Li C-T, Shan M-K (2012) Composing activity groups in social networks. In: Proceedings of ACM international conference on information and knowledge management (CIKM’12), pp 2375–2378Google Scholar
  17. 17.
    Liu W, Sun W, Chen C, Huang Y, Jing Y, Chen K (2012) Circle of friend query in geo-social networks. In: Proceedings of international conference on database systems for advanced applications (DASFAA’12), pp 126–137Google Scholar
  18. 18.
    Majumder A, Datta S, Naidu KVM (2012) Capacitated team formation problem on social networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’12), pp 1005–1013Google Scholar
  19. 19.
    Reich G, Widmayer P (1990) Beyond Steiner’s problem: a VLSI oriented generalization. In: Proceedings of international workshop on graph-theoretic concepts in computer science, pp 196–210Google Scholar
  20. 20.
    Sorkhi M, Hashemi S, Hamzeh A (2011) An effective expert team formation in social networks based on skill grading. In: Proceedings of IEEE international conference on data mining workshops (ICDMW’11), pp 366–372Google Scholar
  21. 21.
    Sozio M, Gionis A (2010) The community-search problem and how to plan a successful cocktail party. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’10), pp 939–948Google Scholar
  22. 22.
    Tong H, Faloutsos C, Pan J-Y (2006) Fast random walk with restart and its application. In: Proceedings of IEEE international conference on data mining (ICDM’06), pp 613–622Google Scholar
  23. 23.
    Tong H, Faloutsos C (2006) Center-piece subgraph: problem definition and fast solution. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’06), pp 404–413Google Scholar
  24. 24.
    Tong H, Qu H, Jamjoom H, Faloutsos C (2009) iPoG: fast interactive proximity querying on graphs. In: Proceedings of ACM international conference on information and knowledge management (CIKM’09), pp 1673–1676Google Scholar
  25. 25.
    Tong H, Faloutsos C, Gallagher B, Eliassi-Rad T (2007) Fast best-effort pattern matching in large attributed graphs. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’07), pp 737–746Google Scholar
  26. 26.
    Wi H, Oh S, Mun J (2009) Jung M (2009) A team formation model based on knowledge and collaboration. Expert Syst Appl 36(5):9121–9134CrossRefGoogle Scholar
  27. 27.
    Yang D-N, Chen Y-L, Lee W-C, Chen M-S (2011) On social-temporal group query with acquaintance constraint. Proc VLDB Endow 4(6):397–408CrossRefGoogle Scholar
  28. 28.
    Yang D-N, Shen C-Y, Lee W-C, Chen M-S (2012) On Socio-spatial group query for location-based social networks. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining (KDD’12), pp 949–957Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Graduate Institute of Networking and MultimediaNational Taiwan UniversityTaipei CityTaiwan
  2. 2.Department of Computer ScienceNational Chengchi UniversityTaipei CityTaiwan

Personalised recommendations