Advertisement

Knowledge Communities and Socio-Cognitive Taxonomies

  • Camille RothEmail author
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Social network analysis (SNA) typically appraises social groups by relying either on interaction patterns or on affiliation similarity. The former case represents the bulk of SNA approaches and relates to the so-called one-mode networks, which are by design blind to actor attributes. The latter case relates to what is denoted as two-mode networks and corresponds to a less abundant literature which uses actor attributes, yet eventually tends to focus much more on actor rather than attribute groups. This chapter aims to show how approaches such as formal concept analysis (FCA) make it possible to appraise actors and attributes on an equal footing. In the particular case of knowledge communities, where actor attributes represent cognitive properties, we deal with joint social and cognitive taxonomies, or socio-cognitive taxonomies. We further demonstrate that FCA also addresses several of the key traditional challenges of community detection in SNA—namely, overlapping groups, hierarchy, and temporal evolution and stability.

Keywords

Community detection Socio-semantic networks Knowledge communities Formal concept analysis Socio-cognitive taxonomies Stability Epistemic communities 

Notes

Acknowledgements

The present contribution partially relies on ideas introduced in a book chapter originally published in French and entitled “Communautés, analyse structurale et réseaux socio-sémantiques” [59].

References

  1. 1.
    Abbott, A.: Things of boundaries. Soc. Res. 62(4), 857–882 (1995)MathSciNetGoogle Scholar
  2. 2.
    Alba, R.D.: A graph-theoretic definition of a sociometric clique. J. Math. Sociol. 3, 113–126 (1973)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Arabie, P., Carroll, J.D.: Conceptions of overlap in social structure. In: Freeman, L.C., White, D.R., Romney, A.K. (eds.) Research Methods in Social Network Analysis, pp. 367–392. George Mason University Press, Fairfax, VA (1989)Google Scholar
  4. 4.
    Balamane, A., Missaoui, R., Kwuida, L., Vaillancourt, J.: Descriptive group detection in two-mode data networks using biclustering. In: Proc. of 2016 IEEE/ACM Intl. Conf. on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Computer Society, San Francisco (2016)Google Scholar
  5. 5.
    Barbut, M., Monjardet, B.: Algèbre et Combinatoire, vol. II. Hachette, Paris (1970)zbMATHGoogle Scholar
  6. 6.
    Bell, C., Newby, H.: Community Studies: An Introduction to the Sociology of the Local Community. Allen & Unwin, London (1972)Google Scholar
  7. 7.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008)CrossRefGoogle Scholar
  8. 8.
    Boeck, P.D., Rosenberg, S.: Hierarchical classes: model and data analysis. Psychometrika 53(3), 361–381 (1988)CrossRefGoogle Scholar
  9. 9.
    Bonacich, P.: Using boolean algebra to analyze overlapping memberships. Sociol. Methodol. 9, 101–115 (1978)CrossRefGoogle Scholar
  10. 10.
    Breiger, R.L.: The duality of persons and groups. Soc. Forces 53(2), 181–190 (1974)CrossRefGoogle Scholar
  11. 11.
    Buzmakov, A., Kuznetsov, S.O., Napoli, A.: Is concept stability a measure for pattern selection? Proc. Comput. Sci. 31, 918–927 (2014)CrossRefGoogle Scholar
  12. 12.
    Capocci, A., Servedio, V., Caldarelli, G., Colaiori, F.: Detecting communities in large networks. Physica A 352, 660–676 (2005)CrossRefGoogle Scholar
  13. 13.
    Cartwright, D., Harary, F.: Structural balance: a generalization of Heider’s theory. Psychol. Rev. 63, 277–292 (1956)CrossRefGoogle Scholar
  14. 14.
    Clauset, A.: Finding local communities in networks. Phys. Rev. E 72, 026132 (2005)CrossRefGoogle Scholar
  15. 15.
    Cohendet, P., Créplet, F., Dupouet, O.: Organisational innovation, communities of practice and epistemic communities: the case of Linux. In: Economics with Heterogeneous Interacting Agents, pp. 303–326. Springer, Berlin (2001)Google Scholar
  16. 16.
    Cowan, R., David, P.A., Foray, D.: The explicit economics of knowledge codification and tacitness. Ind. Corp. Chang. 9(2), 212–253 (2000)CrossRefGoogle Scholar
  17. 17.
    Davis, J.A.: Clustering and structural balance in graphs. Hum. Relat. 20, 181–187 (1967)CrossRefGoogle Scholar
  18. 18.
    Davis, J.A., Leinhardt, S.: The structure of positive interpersonal relations in small groups. In: Berger, J., Zelditch, M., Anderson, B. (eds.) Sociological Theories in Progress. Houghton Mifflin, Boston, MA (1970)Google Scholar
  19. 19.
    Doreian, P.: On the evolution of group and network structure. Soc. Netw. 2, 235–252 (1979)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Doreian, P., Mrvar, A.: A partitioning approach to structural balance. Soc. Netw. 18(2), 149–168 (1996)CrossRefGoogle Scholar
  21. 21.
    Doreian, P., Mrvar, A.: Partitioning signed social networks. Soc. Netw. 31, 1–11 (2009)CrossRefGoogle Scholar
  22. 22.
    Edling, C.R.: Mathematics in sociology. Annu. Rev. Sociol. 28, 197–220 (2002)CrossRefGoogle Scholar
  23. 23.
    Elias, N.: Towards a theory of communities. In: Bell, C., Newby, H. (eds.) The Sociology of Community: A Selection of Readings. Routledge, London (1974)Google Scholar
  24. 24.
    Elzinga, P., Wolff, K., Poelmans, J.: Analyzing chat conversations of pedophiles with temporal relational semantic systems. In: Proc. 1st IEEE European Conference on Intelligence and Security Informatics, pp. 242–249. Odense, Denmark (2012)Google Scholar
  25. 25.
    Everett, M.G.: Role similarity and complexity in social networks. Soc. Netw. 7, 353–359 (1985)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Everett, M.G., Borgatti, S.P.: Analyzing clique overlap. Connections 21(1), 49–61 (1998)Google Scholar
  27. 27.
    Forsyth, E., Katz, L.: A matrix approach to the analysis of sociometric data: preliminary report. Sociometry 9(4), 340–347 (1946)CrossRefGoogle Scholar
  28. 28.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75—174 (2010)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Freeman, L.C.: The sociological concept of ‘group’: an empirical test of two models. Am. J. Sociol. 98(1), 152–166 (1992)CrossRefGoogle Scholar
  30. 30.
    Freeman, L.C.: Un modèle de la structure des interactions dans les groupes. Rev. Fr. Sociol. 36, 743–757 (1995)CrossRefGoogle Scholar
  31. 31.
    Freeman, L.C.: Finding social groups: a meta-analysis of the Southern women data. In: Breiger, R., Carley, K., Pattison, P. (eds.) Dynamic Social Network Modeling and Analysis, pp. 39–97. The National Academies Press, Washington, DC (2003)Google Scholar
  32. 32.
    Freeman, L.C., White, D.R.: Using Galois lattices to represent network data. Sociol. Methodol. 23, 127–146 (1993)CrossRefGoogle Scholar
  33. 33.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin (1999)CrossRefGoogle Scholar
  34. 34.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. PNAS 99, 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Gnatyshak, D., Ignatov, D.I., Semenov, A., Poelmans, J.: Gaining insight in social networks with biclustering and triclustering. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds.) Perspectives in Business Informatics Research BIR 2012: 11th Intl. Conf., Nizhny Novgorod, Russia, Sept 24–26, pp. 162–171. Springer, Berlin (2012)CrossRefGoogle Scholar
  36. 36.
    Haas, P.: Introduction: epistemic communities and international policy coordination. Int. Organ. 46(1), 1–35 (1992)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Heider, F.: Attitudes and cognitive organization. J. Psychol. 21, 107–112 (1946)CrossRefGoogle Scholar
  38. 38.
    Hutchins, E.: Distributed cognition. In: Smelser, N.J., Baltes, P.B. (eds.) International Encyclopedia of the Social and Behavioral Sciences, pp. 2068–2072. Elsevier, Amsterdam (2001)CrossRefGoogle Scholar
  39. 39.
    Klimushkin, M., Obiedkov, S., Roth, C.: Approaches to the selection of relevant concepts in the case of noisy data. In: Kwuida, L., Sertkaya, B. (eds.) Proc. 8th Intl. Conf. Formal Concept Analysis. LNCS/LNAI, vol. 5986, pp. 255–266. Springer, Berlin (2010)CrossRefGoogle Scholar
  40. 40.
    Knorr-Cetina, K.: Scientific communities or transepistemic arenas of research? A critique of quasi-economic models of science. Soc. Stud. Sci. 12(1), 101–130 (1982)Google Scholar
  41. 41.
    Kuznetsov, S.: Stability as an estimate of degree of substantiation of hypotheses derived on the basis of operational similarity. Nauchn. Tekh. Inf. 2(12), 21–29 (1990)Google Scholar
  42. 42.
    Kuznetsov, S., Obiedkov, S., Roth, C.: Reducing the representation complexity of lattice-based taxonomies. In: Priss, U., Polovina, S., Hill, R. (eds.) Conceptual Structures: Knowledge Architectures for Smart Applications: 15th Intl. Conf. on Conceptual Structures, ICCS 2007, Sheffield, UK. LNCS/LNAI, vol. 4604, pp. 241–254. Springer, Berlin (2007)Google Scholar
  43. 43.
    Latapy, M., Magnien, C., Vecchio, N.D.: Basic notions for the analysis of large two-mode networks. Soc. Netw. 30(1), 31–48 (2008)CrossRefGoogle Scholar
  44. 44.
    Lehmann, S., Schwartz, M., Hansen, L.K.: Biclique communities. Phys. Rev. E 78, 016108 (2008)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1(49–80) (1971)CrossRefGoogle Scholar
  46. 46.
    Luce, R.D.: Connectivity and generalized cliques in sociometric group structure. Psychometrika 15, 169–190 (1950)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Luce, R.D., Perry, A.: A method of matrix analysis of group structure. Psychometrika 14, 95–116 (1949)MathSciNetCrossRefGoogle Scholar
  48. 48.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)CrossRefGoogle Scholar
  49. 49.
    Mitra, B., Tabourier, L., Roth, C.: Intrinsically dynamic network communities. Comput. Netw. 56(3), 1041–1053 (2012)CrossRefGoogle Scholar
  50. 50.
    Moody, J.: Peer influence groups: identifying dense clusters in large networks. Soc. Netw. 23, 261–283 (2001)CrossRefGoogle Scholar
  51. 51.
    Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38, 321–330 (2004)CrossRefGoogle Scholar
  52. 52.
    Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  53. 53.
    Palla, G., Barabási, A.L., Vicsek, T.: Quantifying social group evolution. Nature 446, 664–667 (2007)CrossRefGoogle Scholar
  54. 54.
    Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)CrossRefGoogle Scholar
  55. 55.
    Poelmans, J., Ignatov, D.I., Kuznetsov, S.O., Dedene, G.: Formal concept analysis in knowledge processing: a survey on applications. Expert Syst. Appl. 40(16), 6538–6560 (2013)CrossRefGoogle Scholar
  56. 56.
    Pothen, A., Simon, H.D., Liou, K.P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)MathSciNetCrossRefGoogle Scholar
  57. 57.
    Rodriguez, M.A., Pepe, A.: On the relationship between the structural and socioacademic communities of a coauthorship network. J. Informet. 2, 195–201 (2008)CrossRefGoogle Scholar
  58. 58.
    Roth, C.: Binding social and semantic networks. In: Proceedings of ECCS 2006, 2nd European Conference on Complex Systems, Oxford (2006)Google Scholar
  59. 59.
    Roth, C.: Communautés, analyse structurale et réseaux socio-sémantiques. In: Sainsaulieu, I., Salzbrunn, M., Amiotte-Suchet, L. (eds.) Faire communautén société – Dynamique des appartenances collectives, pp. 113–128. Presses Universitaires de Rennes, Rennes (2010)CrossRefGoogle Scholar
  60. 60.
    Roth, C., Bourgine, P.: Lattice-based dynamic and overlapping taxonomies: the case of epistemic communities. Scientometrics 69(2), 429–447 (2006)CrossRefGoogle Scholar
  61. 61.
    Roth, C., Obiedkov, S., Kourie, D.G.: Towards concise representation for taxonomies of epistemic communities. In: Yahia, S.B., Nguifo, E.M. (eds.) Proc. CLA 4th Intl. Conf. on Concept Lattices and Their Applications. LNCS/LNAI, vol. 4923, pp. 240–255. Springer, Berlin (2006)CrossRefGoogle Scholar
  62. 62.
    Roth, C., Cointet, J.P., Obiedkov, S., Romashkin, N.: Analyse textuelle des motions du Congrès de Reims du PS (2008). http://tinyurl.com/39g6lch Google Scholar
  63. 63.
    Ruggie, J.G.: International responses to technology: concepts and trends. Int. Organ. 29(3), 557–583 (1975)CrossRefGoogle Scholar
  64. 64.
    Simmel, G.: The persistence of social groups. Am. J. Sociol. 3(5), 662 (1898)CrossRefGoogle Scholar
  65. 65.
    Soldano, H., Santini, G.: Graph abstraction for closed pattern mining in attributed networks. In: ECAI, pp. 849–854 (2014)Google Scholar
  66. 66.
    Soldano, H., Ventos, V.: Abstract concept lattices. In: Valtchev, P., Jäschke, R. (eds.) Proc. Intl. Conf. on Formal Concept Analysis (ICFCA). LNAI, vol. 6628, pp. 235–250. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  67. 67.
    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with TITANIC. Data Knowl. Eng. 42, 189–222 (2002)CrossRefGoogle Scholar
  68. 68.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)CrossRefGoogle Scholar
  69. 69.
    Wellman, B., Carrington, P.J., Hall, A.: Networks as personal communities. In: Wellman, B., Berkowitz, S.D. (eds.) Social Structures: A Network Analysis, pp. 130–184. Cambridge University Press, Cambridge (1988)Google Scholar
  70. 70.
    White, D.R., Harary, F.: The cohesiveness of block in social networks: node connectivity and conditional density. Sociol. Methodol. 31, 305–359 (2001)CrossRefGoogle Scholar
  71. 71.
    White, D.R., Reitz, K.P.: Graph and semigroup homomorphisms on networks of relations. Soc. Netw. 5, 193–234 (1983)MathSciNetCrossRefGoogle Scholar
  72. 72.
    White, H.C., Boorman, S.A., Breiger, R.L.: Social-structure from multiple networks. I: blockmodels of roles and positions. Am. J. Sociol. 81, 730–780 (1976)Google Scholar
  73. 73.
    Wille, R.: Concept lattices and conceptual knowledge systems. Comput. Math. Appl. 23, 493 (1992)CrossRefGoogle Scholar
  74. 74.
    Wolff, K.: Applications of temporal conceptual semantic systems. In: Wolff, K., Palchunov, D.E., Zagoruiko, N.G. (eds.) Knowledge Processing and Data Analysis. LNAI, vol. 6581, pp. 59–78. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Sciences PoMédialabParisFrance
  2. 2.Centre Marc Bloch Berlin e.V.BerlinGermany

Personalised recommendations