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Abstract

Discovering an effective team of experts toward accomplishing the specific task in social networks has been considered in many real projects. The communication and collaboration among the members and the small cardinality of the team are in the opposite direction to success of the projects. In this paper, we show that the type of a similarity function is also impressive. Its importance is revealed on determining what similar or dissimilar experts should be selected or rejected in the process of the assignment. Considering the graph of underlying social network as conceptual social networking websites, we attribute the team formation problem as a vertices similarity environment based on their common neighbors regarding their co-authored papers. Also, the implicit similarities are used with respect to inattention of additional intermediates between any two nodes in the graph. In addition, taking inspiration from human–human interactions, using just the implicit vertex similarities propose a collaborative recommendation that is based on the team formation framework. They can also identify effectors in social networks established by the structural equivalence relation. Thus, they make the algorithm faster on searching for members of the team. Moreover, the proficiency similarity measures of authors are considered as their potential characteristics that measure their skillfulness level and real contribution corresponding to the required skill. The combination of similarity measures in the cost function causes the algorithm to search the more effective team specially in equal situations. The experimental results on DBLP co-authorship graph show the effectiveness of using the new similarity measures in the proposed method.

Keywords

Social networks team of experts similarity measures

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References

[1]

Ahlgren,

Jarvening and

Rousseau, Requirements for a cocitation similarity measure, with special reference to Pearsonś correlation coefficient, Journal of the American Society for Information Science and Technology 54(6) (2003), 550–560.

[2]

Anagnostopoulos,

Becchetti and

Castillo, Power in unity: Forming teams in large-scale community systems, in: CIKM’10, 2010.

[3]

Backstrom,

D.P.

Huttenlocher,

J.M.

Kleinberg and

Lan, Group formation in large social networks: Membership, growth and evolution, in: Proc. the ACM Intl. Conference on Knowledge Discovery and Data Mining KDD, 2006, pp. 44–54.

[4]

A.L.

Barabási and

Albert, Emergence of scaling in random networks, Science 286 (1999), 509–512.

[5]

Baykasoglu,

Dereli and

Das, Project team selection using fuzzy optimization approach, Journal of Cybernetics and Systems 38 (2007), 155–185.

[6]

Boon and

Siersksma, Team formation: Matching quality supply and quality demand, European Journal of Operational Research 148 (2003), 277–292.

[7]

Candillier,

Meyer and

Fessant, Designing specific weighted similarity measures to improve collaborative filtering systems, in: Proc. the IEEE Intl. Conference on Data Mining ICDM, 2008, pp. 242–255.

[8]

Cheatham and

Cleereman, Application of social network analysis to collaborative team formation, in: Proc. the Intl. Symposium on Collaborative Technologies and Systems, 2006, pp. 306–311.

[9]

H.-H.

Chen,

Gou,

Zhang and

C.L.

Giles, CollabSeer: A search engine for collaboration discovery, in: JCDL’11, 2011, pp. 231–240.

10.

[10]

S.J.

Chen and

Lin, Modeling team member characteristics for the formation of a multifunctional team in concurrent engineering, IEEE Transactions on Engineering Management 51 (2004), 111–124.

11.

[11]

Dereli,

Baykasoglu and

Sena Das, Fuzzy quality-team formation for value added auditing: A case study, Journal of Engineering and Technology Management 24 (2007), 366–394.

12.

[12]

Deshpande and

Karypis, Item based top-N recommendation algorithms, ACM Transactions on Information Systems 22 (2004), 143–177.

13.

[13]

Farhadi,

Sorkhi,

Hashemi and

Hamzeh, An effective expert team formation in social networks based on skill grading, in: ICDM Workshops’11, 2011, pp. 366–372.

14.

[14]

E.L.

Fitzpatrick and

R.G.

Askin, Forming effective worker teams with multi-functional skill requirements, Journal of Computers & Industrial Engineering 48 (2005), 593–608.

15.

[15]

Gaston,

Simmons and

DesJardins, Adapting network structures for efficient team formation, in: Proc. the AAMAS-04 Workshop on Learning and Evolution in Agent-Based Systems, 2004.

16.

[16]

Hamers,

Hemeryck,

Herweyers,

Janssen,

Keters,

Rousseau and

Vanhoutte, Similarity measures in scientometric research: The Jaccard index versus Salton’s cosine formula, Information Processing & Management 25(3) (1989), 315–318.

17.

[17]

Kargar and

Aijun, Discovering top-k teams of experts with/without a leader in social networks, in: CIKM, 2011, pp. 985–994.

18.

[18]

Karypis, Evaluation of item-based top-N recommendation algorithms, in: Proc. the ACM Intl. Conference on Information and Knowledge Management, CIKM, 2001, pp. 247–254.

19.

[19]

Kleinberg, Authoritative sources in a hyperlinked environment, Journal of the ACM (JACM) 46(5) (1999), 604–632.

20.

[20]

A.D.

Korwin,

M.F.

Shipley and

Kleyle, Utilizing fuzzy compatibility of skill sets for team selection in multi-phase projects, Journal of Engineering and Technology Management 19 (2002), 307–319.

21.

[21]

Lappas,

Liu and

Terzi, Finding a team of experts in social networks, in: Proc. of ACM Intl. Conference on Knowledge Discovery and Data Mining (KDD’09), 2009, pp. 467–476.

22.

[22]

Leicht,

Holme and

Newman, Vertex similarity in networks, Physical Review E 73(2) (2006), 26120.

23.

[23]

Leskovec,

Huttenlocher and

Kleinberg, Predicting positive and negative links in online social networks, in: WWW’10, 2010.

24.

[24]

Li and

Shan, Team formation for generalized tasks in expertise social networks, in: Proc. the 2nd SocialCom/PASSAT, August 2010, 2010, pp. 9–16.

25.

[25]

Linden,

Smith and

York, Recommendations: Item to item collaborative filtering, IEEE Internet Computing 7 (2003), 76–80.

26.

[26]

M.K.

Malhotra,

Grover and

Desilvio, Reengineering the new product development process: A framework for innovation and flexibility in high technology firms, Omega 24(4) (1996), 425–441.

27.

[27]

Milo,

Shen-Orr,

Itzkovitz,

Kashtan,

Chklovskii and

Alon, Network motifs: Simple building blocks of complex networks, Science 298 (2002), 824–827.

28.

[28]

Nan Liu and

Yang, EigenRank: A ranking-oriented approach to collaborative filtering, in: SIGIR, 2008, pp. 83–90.

29.

[29]

M.E.J.

Newman, The structure and function of complex networks, SIAM Review 45(2) (2003), 167–256.

30.

[30]

Ravasz,

Somera,

Mongru,

Oltvai and

Barabási, Hierarchical organization of modularity in metabolic networks, Science 297(5586) (2002), 1551.

31.

[31]

Resnick,

Iacovou,

Suchak,

Bergstrom and

Riedl, GroupLens: An open architecture for collaborative filtering of net news, in: Proc. the Conference on Computer Supported Cooperative Work, ACM, 1994, pp. 175–186.

32.

[32]

Salas,

T.L.

Dickinson,

S.A.

Converse and

S.I.

Tannenbaum, Toward an understanding of team performance and training, in: Team: Their Training and Performance,

Swezey and

Salas, eds, NJ Ablex, Norwood, 1992.

33.

[33]

Salton, Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer, Addison-Wesley, 1989.

34.

[34]

Sarwar,

Karypis,

Konstan and

Reidl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th International Conference on World Wide Web, ACM, 2001, pp. 285–295.

35.

[35]

Tan,

Steinbach and

Kumar, Introduction to Data Mining, Addison-Wesley, 2006.

36.

[36]

Van Eck and

Waltman, Appropriate similarity measures for author cocitation analysis, ERIM Report Series Refence No. ERS-091-LIS, 2007.

37.

[37]

J.M.L.

Van Engelen,

D.J.

Kiewiet and

Terlouw, Improving performance of product development teams through managing polarity, International Studies of Management and Organization 31(1) (2001), 46–63.

38.

[38]

Wasserman, Social Network Analysis: Methods and Applications, Cambridge Univ. Press, Cambridge, 1994.

39.

[39]

D.J.

Watts and

S.H.

Strogatz, Collective dynamics of small-world networks, Nature 393(6684) (1998), 440–442.

40.

[40]

Wi,

Oh,

Mun and

Jung, A team formation model based on knowledge and collaboration, Expert Syst. Appl. 36(5) (2009), 9121–9134.

41.

[41]

Xiang,

Neville and

Rogati, Modeling relationship strength in online social networks, in: WWW’10. 2010.

42.

[42]

Zakarian and

Kusiak, Forming teams: An analytical approach, IIE Transactions 31 (2004), 85–97.

Effective team formation in collaboration networks using vertex and proficiency similarity measures

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Keywords

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