Advertisement

Informative Value of Individual and Relational Data Compared Through Business-Oriented Community Detection

  • Vincent LabatutEmail author
  • Jean-Michel Balasque
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)

Abstract

Despites the great interest caused by social networks in Business Science, their analysis is rarely performed in both a global and systematic way in this field. This could be explained by the fact their practical extraction is a difficult and costly task. One may ask if equivalent information could be retrieved from less expensive, individual data (i.e. describing single individuals instead of pairs). In this work, we try to address this question through group detection. We gather both types of data from a population of students, estimate groups separately using individual and relational data, and obtain sets of clusters and communities, respectively. We measure the overlap between clusters and communities, which turns out to be relatively weak. We also define a predictive model, allowing us to identify the most discriminant attributes for the communities, and to reveal the presence of a tenuous link between the relational and individual data. Our results indicate both types of data convey considerably different information in this specific context, and can therefore be considered as complementary. To emphasize the interest of communities for Business Science, we also conduct an analysis based on hobbies and purchased brands.

Keywords

Social Network Mobile Phone Relational Data Community Detection Factual Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We would like to thank Günce Orman, who helped organizing and translating the survey, Siegfried Devoldère who also translated parts of the questions, and Taleb Mohamed El Wely who programmed the electronic form and designed the survey website. Our gratitude also goes to the reviewers, who provided us constructive comments and allowed us to improve the quality of this chapter.

References

  1. 1.
    Baret, C., Huault, I., Picq, T.: Management et réseaux sociaux: Jeux d’ombres et de lumières sur les organisations. Revue Française de Gestion 32(163), 93–106 (2006)CrossRefGoogle Scholar
  2. 2.
    Comet, C.: Productivité et réseaux sociaux: Le cas des entreprises du bâtiment. Revue Française de Gestion 32(163), 155–169 (2006)CrossRefGoogle Scholar
  3. 3.
    Simon, F., Tellier, A.: Créativité et réseaux sociaux dans l’organisation ambidextre. Revue Française de Gestion 187, 145–159 (2008)CrossRefGoogle Scholar
  4. 4.
    Ferrary, M.: Apprentissage Collaboratif et réseaux d’investisseurs en capital-risque. Revue Française de Gestion 163, 171–181 (2006)CrossRefGoogle Scholar
  5. 5.
    Ranie-Didice, B.: Capital social des dirigeants et performance des entreprises. Revue des Sciences de Gestion 231/232, 131–135 (2008)Google Scholar
  6. 6.
    Fondeur, Y., Lhermitte, F.: Réseaux sociaux numériques et marché du travail. Revue de l’Ires 52(3), 102–131 (2006)Google Scholar
  7. 7.
    Guieu, G., Meschi, P.-X.: Conseils d’administrations et réseaux d’administration en Europe. Revue Française de Gestion 185, 21–45 (2008)CrossRefGoogle Scholar
  8. 8.
    Dwyer, P.: Measuring the value of electronic word of mouth and its impact in consumer communities. J. Interact. Market. 21(2), 16 (2007)CrossRefGoogle Scholar
  9. 9.
    Goldenberg, J., Han, S., Lehman, D.R., Hong J.W.: The role of hubs in the adoption process. J. Market. 73, 1–13 (2009)CrossRefGoogle Scholar
  10. 10.
    Steyer, A., Garcia-Bardidia, R., Quester, P.: Modélisation de la structure sociale de groupes de discussion sur Internet: implications pour le contrôle du marketing viral. Rech. Appl. Market. 22(3), 29–44 (2007)CrossRefGoogle Scholar
  11. 11.
    van der Merwe, R., van Heerden, G.: Finding and utilising opinion leaders: social networks and the power of relationships. S. Afr. J. Bus. Manag. 40(3), 65–73 (2009)Google Scholar
  12. 12.
    Hyunsook, K.: Comparing fashion process networks and friendship networks in small groups of adolescents. J. Fash. Market. Manag. 12(4), 545–564 (2008)Google Scholar
  13. 13.
    Hartmann, W.R., Manchanda, P., Nair, H., Hosanagar, K., Tucker, C.: Modeling social interactions: identification, empirical methods and policy implications. Market. Lett. 19, 287–304 (2008)CrossRefGoogle Scholar
  14. 14.
    Watts, D.C., Dodds, P.S.: Influentials, networks and public opinion formation. J. Consum. Res. 34, 441–458 (2007)CrossRefGoogle Scholar
  15. 15.
    Iacobucci, D., Hopkins, N.: Modeling dyadic interactions and networks in marketing. J. Market. Res. 29(1), 5–20 (1992)CrossRefGoogle Scholar
  16. 16.
    Burt, R.: Structural Holes and Good Ideas. Am. J. Sociol. 110(2), 349–399 (2004)CrossRefGoogle Scholar
  17. 17.
    Perry-Smith, J.E.: Social yet creative: the role of social relationships in facilitating individual creativity. Acad. Manag. J. 49(1), 85–101 (2006)CrossRefGoogle Scholar
  18. 18.
    Rose, D., Charbonneau, J., Carrasco, P.: La constitution de liens faibles: une passerelle pour l’adaptation des immigrantes centro-americaines mères de jeunes enfants a Montréal Canadian Ethnic Studies 33(1), 73–91 (1999)Google Scholar
  19. 19.
    Granovetter, M.: The impact of social structure on economic outcomes. J. Econ. Perspect. 19(1), 33–50 (2005)CrossRefGoogle Scholar
  20. 20.
    Sureh, C., Srividya, G., Swetha, K.: Viral distribution potential based on active node identification for ad distribution in viral networks. Int. J. Mobile Market. 4(1), 48–56 (2009). (http://connection.ebscohost.com/c/articles/43884907/viral-distribution-potential-based-active-node-identification-ad-distribution-viral-networks)
  21. 21.
    Chollet, B.: L’analyse des réseaux personnels dans les organisations: quelles données utiliser? Revue Finance Contrôle Stratégie 11(1), 105–130 (2008)Google Scholar
  22. 22.
    Doyle, S.: The role of social networks in marketing. J. Database Market. Cust. Strategy Manag. 15, 60–64 (2007)CrossRefGoogle Scholar
  23. 23.
    Droulers, O., Roullet, B.: Emergence du neuromarketing: apports et perspectives pour les praticients et les chercheurs. Décis. Market. 46, 9–22 (2007). (http://www.afm-marketing.org/1-afm-association-francaise-du-marketing/126-afmnet/document.aspx?id=4123)
  24. 24.
    Ohme, R., Reykowska, D., Wiener, D., Choromanska, A.: Application of frontal EEG asymmetry to advertising research. J. Econ. Psychol. 31(5), 785–794 (2010)CrossRefGoogle Scholar
  25. 25.
    Parlebas, P.: Sociométrie, réseaux et Communication. PUF, Paris (1992)Google Scholar
  26. 26.
    Evrard, Y., Pras, B., Roux, E.: MARKET: Etudes et recherches en Marketing. Dunod, Paris (2000)Google Scholar
  27. 27.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)Google Scholar
  28. 28.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefGoogle Scholar
  29. 29.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. Paper presented at the International Conference on Knowledge Discovery and Data Mining, Portland, USA, pp. 226–231 (1996)Google Scholar
  30. 30.
    Zhang, T., Ramakrishnon, R., Livny, M.: BIRCH: an efficient data clustering method for very large datebases. Paper presented at the International Conference on Management of Data, Montreal, Canada, pp. 103–114 (1996)Google Scholar
  31. 31.
    R Development Core Team: R: A Language and Environment for Statistical Computing. In. R Foundation for Statistical Computing, Vienna, Austria (2009)Google Scholar
  32. 32.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)CrossRefGoogle Scholar
  33. 33.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  34. 34.
    Tasgin, M., Herdagdelen, A., Bingol, H.: Community Detection in Complex Networks Using Genetic Algorithms. arXiv:0711.0491 (2007). (http://arxiv.org/abs/0711.0491)
  35. 35.
    Newman, M.E.J.: Modularity and community structure in networks. PNAS USA 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  36. 36.
    Newman, M.E.J.: Analysis of weighted networks. Phys. Rev. E 70(5) (2004)Google Scholar
  37. 37.
    Leicht, E.A., Newman, M.E.J.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)CrossRefGoogle Scholar
  38. 38.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. Int. J. Complex Syst. 695)(2006)Google Scholar
  39. 39.
    Donetti, L., Munoz, M.A.: Detecting network communities: a new systematic and efficient algorithm. J. Stat. Mech. (10), P10012 (2004). (http://iopscience.iop.org/1742-5468/2004/10/P10012)
  40. 40.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  41. 41.
    Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)CrossRefGoogle Scholar
  43. 43.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. PNAS 105(4), 1118 (2008)CrossRefGoogle Scholar
  44. 44.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)CrossRefGoogle Scholar
  45. 45.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. P10008 (2008). (http://iopscience.iop.org/1742-5468/2008/10/P10008/)
  46. 46.
    van Dongen, S.: Graph clustering via a discrete uncoupling process. SIAM J. Matrix Anal. Appl. 30(1), 121–141 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  47. 47.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. PNAS 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  48. 48.
    Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74(1), 016110 (2006)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Pons, P., Latapy, M.: Computing communities in large networks using random walks. Proceedings of the Computer and Information Sciences – Iscis 2005, Istanbul, vol. 3733, pp. 284–293 (2005)Google Scholar
  50. 50.
    Barnes, E.R.: An algorithm for partitioning the nodes of a graph. SIAM J. Algebr. Discret Method 3, 541–550 (1982)zbMATHCrossRefGoogle Scholar
  51. 51.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  52. 52.
    Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)CrossRefGoogle Scholar
  53. 53.
    Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)CrossRefGoogle Scholar
  54. 54.
    Derenyi, I., Palla, G., Vicsek, T.: Clique percolation in random networks. Phys. Rev. Lett. 94(16) (2005)Google Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Computer Science DepartmentGalatasaray UniversityOrtaköy/IstanbulTurkey
  2. 2.Business Science and Marketing DepartmentGalatasaray UniversityOrtaköy/IstanbulTurkey

Personalised recommendations