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Forming a well-connected team of experts based on a social network graph: a novel weighting approach

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Abstract

This paper studies the team formation problem, which includes a social collaboration network and a project, comprised of a number of tasks, each requiring a particular skill. The goal is to select the most appropriate members for the team, who not only cover the required project skills, but also have the highest degree of expertise according to the team cost. This study presents a more comprehensive initial definition of team formation in which each task is performed by a specific number of experts. Moreover, a method is proposed, which selects the best team, such that the selected members have the highest degree of cooperation for each task. The distance function is also changed to provide a more logical calculation of the shortest path between experts. Furthermore, some team members have more relationships with others, which makes them more important members in a team. This is an important factor in computing the communication cost of team members. Empirical results on DBLP dataset show the superiority of the proposed approach. Moreover, the proposed method reduces project risk by considering team members’ levels of expertise and insures the success of the given project.

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Notes

  1. https://drive.google.com/file/d/0ByrwV_lIgJ-abVFxV05Uc3dOc3c/view?usp=sharing.

References

  • Al Hasan M, Zaki MJ (2011) A survey of link prediction in social networks. In: Aggarwal C (eds) Social network data analytics. Springer, Boston, pp 243–275

    Chapter  Google Scholar 

  • Anagnostopoulos A, Becchetti L, Castillo C, Gionis A, Leonardi S (2012) Online team formation in social networks. In: Proceedings of the 21st international conference on world wide web. ACM, New York, pp 839–848

  • Appel AP, Cavalcante VF, Vieira MR, de Santana VF, de Paula RA, Tsukamoto SK (2014) Building socially connected skilled teams to accomplish complex tasks. In: Proceedings of the 8th workshop on social network mining and analysis. ACM, New York, p 8

  • Ashenagar B, Hamzeh A, Eghlidi NF, Afshar A (2015) A fast approach for multi-objective team formation in social networks. In: 2015 7th conference on information and knowledge technology (IKT), pp 1–6

  • Awal GK, Bharadwaj K (2014) Team formation in social networks based on collective intelligence-an evolutionary approach. Appl Intell 41(2):627–648

    Article  Google Scholar 

  • Batagelj V, Zaversnik M (2003) An \(o(m)\) algorithm for cores decomposition of networks. Preprint arXiv:cs/0310049

  • Bhowmik A, Borkar V, Garg D, Pallan M (2014) Submodularity in team formation problem. In: Proceedings of the 2014 SIAM international conference on data mining, Philadelphia, pp 893–901

  • Chen W, Wang Y, Yang S (2009) Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 199–208

  • Chhabra M, Das S, Szymanski B (2013) Team formation in social networks. In: Gelenbe E, Lent R (eds) Computer and information sciences III. Springer, London, pp 291–299

    Chapter  Google Scholar 

  • Clauset A (2005) Finding local community structure in networks. Phys Rev E 72(2):026132

    Article  Google Scholar 

  • DuBois T, Golbeck J, Srinivasan A (2011) Predicting trust and distrust in social networks. In: 2011 IEEE 3rd international conference on privacy, security, risk and trust (PASSAT) and 2011 IEEE 3rd international conference on social computing (Socialcom). IEEE, New York, pp 418–424

  • Farhadi F, Sorkhi M, Hashemi S, Hamzeh A (2012a) An effective framework for fast expert mining in collaboration networks: a group-oriented and cost-based method. J Comput Sci Technol 27(3):577–590

    Article  MathSciNet  Google Scholar 

  • Farhadi F, Hoseini E, Hashemi S, Hamzeh A (2012b) Teamfinder: a co-clustering based framework for finding an effective team of experts in social networks. In: 2012 IEEE 12th international conference on data mining workshops. IEEE, New York, pp 107–114

  • Fazli M, Ghazimatin A, Habibi J, Haghshenas H (2014) Team selection for prediction tasks. J Comb Optim 31:1–15

    MathSciNet  MATH  Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    Article  MathSciNet  Google Scholar 

  • Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM SIGMOD Rec 42(2):17–28

    Article  Google Scholar 

  • Juang M, Huang C, Huang J (2013) Efficient algorithms for team formation with a leader in social networks. J Supercomput 66(2):721–737

    Article  Google Scholar 

  • Kargar M, An A, Zihayat M (2012) Efficient bi-objective team formation in social networks. In: Flach PA, De Bie T, Cristianini N (eds) Machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 483–498

    Chapter  Google Scholar 

  • Kargar M, Zihayat M, An A (2013) Affordable and collaborative team formation in an expert network. Technical report CSE-2013, vol 1, Department of Computer Science and Engineering, York University

  • Lappas T, Liu K, Terzi E (2009) Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 467–476

  • Liu H, Qiao M, Greenia D, Akkiraju R, Dill S, Nakamura T, Song Y, Nezhad HM (2014) A machine learning approach to combining individual strength and team features for team recommendation. In: 2014 13th international conference on machine learning and applications (ICMLA). IEEE, New York, pp 213–218

  • Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A 390(6):1150–1170

    Article  Google Scholar 

  • Rodriguez MG, Schölkopf B (2012) Influence maximization in continuous time diffusion networks. Preprint arXiv:1205.1682

  • Sherchan W, Nepal S, Paris C (2013) A survey of trust in social networks. ACM Comput Surv (CSUR) 45(4):47

    Article  Google Scholar 

  • Wang W, Jiang Y (2015) Multiagent-based allocation of complex tasks in social networks. IEEE Trans Emerg Top Comput PP(99):1

    Google Scholar 

  • Wi H, Oh S, Mun J, Jung M (2009) A team formation model based on knowledge and collaboration. Expert Syst Appl 36(5):9121–9134

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran

    Bahareh Ashenagar & Ali Hamzeh

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  1. Bahareh Ashenagar
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  2. Ali Hamzeh
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Correspondence to Ali Hamzeh.

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Ashenagar, B., Hamzeh, A. Forming a well-connected team of experts based on a social network graph: a novel weighting approach. Soc. Netw. Anal. Min. 9, 48 (2019). https://doi.org/10.1007/s13278-019-0592-8

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  • DOI: https://doi.org/10.1007/s13278-019-0592-8

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