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Part of the book series: Studies in Computational Intelligence ((SCI,volume 311))

Abstract

The WWW, has become a fertile land where anyone can transform his ideas into real applications to create new amazing services. Therefore, it was just a matter of time until the massive proliferation of virtual communities, social networks, etc. New social structures have been formed by massive use of new technologies. This way, people can relate to other by interests, experiences or needs. In a scenario where WWW has become more important every day, and people is using more often the web to relate to others, to read news, obtain tickets, etc. The need of well organized web sites has become one of the vital goals of enterprises and organizations. To accomplish such task web mining area was born more than a decade ago. Web mining are techniques that help managers (or web sites’ experts) to extract information from a web sites’ content, link structure or visitors’ browsing behavior. This way, it is possible to enhance a web site, obtain visitors’ interests patterns to create new services, or provide very specific adds depending on the navigation preferences of visitors (recommendations systems). In the beginning of the Web, web sites were formed by static pages, this means contents were created usually by the owner of the web sites, or the web masters. These contents usually did not change very much through time since it required effort from administrators. Today, a new paradigm arose, we have a participative Web. The web has evolved to the point that it is composed by dynamic contents created by millions of users collaborating one to each other. Sites like, youtube, Blogger, Twitter, facebook, orkut, flickr, among many other, are part of the social web sites’ phenomenon. For example, twitter had 475,000 members by Feb. 2008 while it had 7,038,000 members by Feb. 2009, which means 1382% of growth. Facebook on the same dates passed from 20,043,000 members to 65,704,000 members which means 228%. The use of web intelligence techniques to explode data stored in these social web has become a natural approach to obtain knowledge from them. Since volumes of data are huge, the use of web intelligence techniques was the natural approach to obtain knowledge from social web sites. However, to study members of a social web site is not only to study a group of people accessing a web site and working together; they establish social relationships through the use of Internet tools allowing the formation shared identity and a shared sense of the world. In order to provide truly valuable information to help managers, web masters and to provide better members’ experience when using the social web site, it is necessary to take into account datas’ social nature in web mining techniques. This chapter focuses on the application web intelligence techniques in combination to social network analysis to study of social web sites. In order to provide truly valuable informaton from social web sites that support a social entity. We show that new techniques need to be focused on the study of underlying social aspects of those social entities to really exploit the datas’ social nature and provide a better understandig of human relationships.

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References

  1. Alvarez, H., Ríos, S., Merlo, E., Aguilera, F., Guerrero, L.: Enhancing sna with a concept-based text mining approach to discover key members on a vcop p (to appear, 2010)

    Google Scholar 

  2. Arenas, A., Danon, L., Díaz-Guilera, A., Gleiser, P.: Community analysis in social networks. The European Physical Journal B-Condensed Matter (2004), http://www.springerlink.com/index/8R9TW62UPVXMFM9A.pdf

  3. Barab, S.: Designing for virtual communities in the service of learning. The Information Society (2003), http://www.informaworld.com/index/LQTX8NCUXP3BT3QF.pdf

  4. Benevenuto, F., Rodrigues, T., Cha, M.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, pp. 49–62 (2009), http://portal.acm.org/citation.cfm?id=1644893.1644900&coll=portal&dl=ACM&type=series&idx=SERIES10693&part=series&WantType=Proceedings&title=IMC

  5. Bourhis, A., Dubé, L., Jacob, R.: The success of virtual communities of practice: The leadership factor. The Electronoc Journal of Knowledge Management 3(1), 23–34 (2005), http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.9460&rep=rep1&type=pdf

    Google Scholar 

  6. Breiger, R.: Duality of persons and groups, the. Soc. F (1974), http://heinonlinebackup.com/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/josf53&section=26

  7. Breiger, R.L., Carley, K.M., Pattison, P.: On Human Factors. N.R.C.U.C.: Dynamic social network modeling and analysis: workshop summary, vol. 2002, p. 379 (2003), http://books.google.com/books?id=IfnYO3YeZ_0C&printsec=frontcover

  8. Breslin, J., Decker, S.: The future of social networks on the internet: The need for semantics. IEEE Internet Computing 11(6), 86–90 (2007), http://www.google.com/search?client=safari&rls=en-us&q=The+Future+of+Social+Networks+on+the+Internet:+The+Need+for+Semantics&ie=UTF-8&oe=UTF-8

    Article  Google Scholar 

  9. Carotenuto, L., Etienne, W., Fontaine, M., Friedman, J., Muller, M., Newberg, H., Simpson, M., Slusher, J., Stevenson, K.: Communityspace: Toward flexible support for voluntary knowledge communities. In: Proc. of Workshop on Workspace Models for Collaboration (1999), http://domino.watson.ibm.com/cambridge/research.nsf/0/0e8c8166a02d5338852568f800634af1/FILE/communityspace.PDF

  10. Chakrabarti, S.: Mining the web: discovering knowledge from hypertext data.  Part 2, 345 (2003), http://books.google.com/books?id=5Zxw1h6yc_UC&printsec=frontcover

  11. Chakrabarti, S., Dom, B., Gibson, D., Kleinberg, J.: Mining the link structure of the world wide web. IEEE Computer (1999), http://www.cs.cornell.edu/home/kleinber/ieee99-web.pdf

  12. Cooley, R., Mobasher, B., Srivastava.., J.: Data preparation for mining world wide web browsing patterns. Knowledge and Information Systems 1(1) (1999), http://maya.cs.depaul.edu/~mobasher/papers/webminer-kais.pdf

  13. Craven, P., Wellman, B.: The network city, p. 126 (1973), http://books.google.com/books?id=MCSLGAAACAAJ&printsec=frontcover

  14. Dai, H., Mobasher, B.: A road map to more effective web personalization: Integrating domain knowledge with web usage mining. In: Proceedings of the International Conference on Internet Computing 2003 (2003), http://maya.cs.depaul.edu/~mobasher/papers/DM03.pdf

  15. Datta, A., Dutta, K., VanderMeer, D., Ramamritham, K.: An architecture to support scalable online personalization on the web. The VLDB Journal (2001), http://www.springerlink.com/index/8W4MV36RT5D1BB42.pdf

  16. Ehrlich, K., Lin, C., Griffiths-Fisher, V.: Searching for experts in the enterprise: combining text and social network analysis. In: Proceedings of the 2007 international ACM conference on Supporting group work (2007), http://portal.acm.org/citation.cfm?id=1316642

  17. Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Transactions on Internet Technology, TOIT (2003), http://portal.acm.org/citation.cfm?id=643478

  18. Eirinaki, M., Vazirgiannis, M., Varlamis, I.: Sewep: using site semantics and a taxonomy to enhance the web personalization process. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003), http://portal.acm.org/citation.cfm?id=956765

  19. Falkowski, T., Bartelheimer, J., Spiliopoulou, M.: Mining and visualizing the evolution of subgroups in social networks. In: WI 2006: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence (2006), http://portal.acm.org/citation.cfm?id=1248823.1249048

  20. Falkowski, T., Spiliopoulou, M.: Data mining for community dynamics. Künstliche Intelligenz (2007), http://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2007-03_page23-29_web_full.pdf

  21. Fetterman, D.: Ethnography: Step by step. orton.catie.ac.cr (1998), http://orton.catie.ac.cr/cgi-bin/wxis.exe/?IsisScript=SIBE01.xis&method=post&formato=2&cantidad=1&expresion=mfn=034353

  22. Fredericks, K., Durland, M.: The historical evolution and basic concepts of social network analysis. New directions for evaluation (2006), http://www3.interscience.wiley.com/journal/112391275/abstract

  23. Garton, L., Haythornthwaite, C., Wellman, B.: Studying online social networks. Journal of Computer-Mediated Communications 3(1) (1997), http://www.google.com/search?client=safari&rls=en-us&q=Studying+online+social+networks&ie=UTF-8&oe=UTF-8

  24. Géry, M., Haddad, H.: Evaluation of web usage mining approaches for user’s next request prediction. In: Proceedings of the 5th ACM international workshop on Web information and data management (2003), http://portal.acm.org/citation.cfm?id=956716

  25. Henri, F., Pudelko, B.: Understanding and analysing activity and learning in virtual communities. Journal of Computer Assisted Learning 19, 474–487 (2003), http://hal.archives-ouvertes.fr/hal-00190267/

    Article  Google Scholar 

  26. Hong, L., Davison, B.: A classification-based approach to question answering in discussion boards. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp. 171–178 (2009), http://www.springerlink.com/content/l7l7p16267004158/

  27. Huang, W., Hong, S.H., Eades, P.: How people read sociograms: a questionnaire study. In: APVis 2006: Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation, vol. 60 (2006), http://portal.acm.org/citation.cfm?id=1151903.1151932

  28. Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004), http://portal.acm.org/citation.cfm?id=1014052.1014076

  29. Johnson, C.: A survey of current research on online communities of practice, The internet and higher education. Elsevier, Amsterdam (2001), http://linkinghub.elsevier.com/retrieve/pii/S1096751601000471

    Google Scholar 

  30. Jones, Q.: Virtual-communities, virtual settlements & cyber-archaeology: A theoretical outline. Journal of Computer Mediated Communication (1997), http://jcmc.indiana.edu/vol3/issue3/jones.html?ref=totalcasinoguide.info

  31. Kim, A.J.: Community Building on the Web: Secret Strategies for Successful Online Communities (2000), http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20%5C&path=ASIN/0201874849

  32. Kim, W., Jeong, O.R., Lee, S.W.: On social web sites. Information Systems 35(2), 215–236 (2010), http://www.sciencedirect.com/science/article/B6V0G-4X5YT84-1/%2/84a742613988ab4ee8b8f2ff0bd7ae54

    Article  Google Scholar 

  33. Koh, J., Kim, Y., Butler, B., Bock, G.: Encouraging participation in virtual communities. Communications of the ACM (2007), http://portal.acm.org/citation.cfm?id=1216016.1216023

  34. Kosala, R., Blockeel, H.: Web mining research: a survey. ACM SIGKDD Explorations Newsletter (2000), http://portal.acm.org/citation.cfm?id=360406

  35. Kozinets, R.: The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research (2002), http://www.atypon-link.com/AMA/doi/abs/10.1509/jmkr.39.1.61.18935

  36. Liu, H.: Kešelj, V.: Combined mining of web server logs and web contents for classifying user navigation patterns and predicting users’ future requests. In: Data & Knowledge Engineering (2007), http://portal.acm.org/citation.cfm?id=1231807

  37. Loh, S., de Oliveira, J., Gameiro, M.: Knowledge discovery in texts for constructing decision support systems. Applied Intelligence (2003), http://www.springerlink.com/index/L7L5787M0J82J0WN.pdf

  38. Marathe, J.: Creating community online. Durlacher Research Ltd. (1999), http://www.delijst.net/delijst/pdf/unpan003006.pdf

  39. Marsden, P., Lin, N.: Social structure and network analysis (1982), http://en.scientificcommons.org/22431038

  40. Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Communications of the ACM (2000), http://portal.acm.org/citation.cfm?doid=345124.345169

  41. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Workshop On Web Information And Data Management (2001), http://portal.acm.org/citation.cfm?id=502932.502935

  42. Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J.: Integrating web usage and content mining for more effective personalization. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds.) EC-Web 2000. LNCS, vol. 1875, pp. 165–176. Springer, Heidelberg (2000), http://books.google.com/books?hl=en&lr=&ie=UTF-8&id=kb69hBiQMiYC&oi=fnd&pg=PA165&dq=fbGsqm_a8JcJ:scholar.google.com/&ots=6WCTl9IzVr&sig=bMwSGS73lyQlzN6cVewJp7dlNg8

    Chapter  Google Scholar 

  43. Mulvenna, M., Anand, S., Büchner, A.: Personalization on the net using web mining: Introduction. Communications of the ACM (2000), http://portal.acm.org/citation.cfm?doid=345124.345165

  44. Nasraoui, O., Soliman, M., Saka, E., Badia, A., Germain, R.: A web usage mining framework for mining evolving user profiles in dynamic web sites. IEEE Transactions on Knowledge and Data Engineering (2008), http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.190667

  45. Nocera, J.: Ethnography and hermeneutics in cybercultural research accessing irc virtual communities. Journal of Computer-Mediated Communication (2002), http://www.blackwell-synergy.com/doi/abs/10.1111/j.1083-6101.2002.tb00146.x

  46. Pal, S., Talwar, V., Mitra, P.: Web mining in soft computing framework: relevance, state of the art and future directions. Neural Networks (2002), http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1031947

  47. Perkowitz, M.: Adaptive web sites: Cluster mining and conceptual clustering for index page synthesis. perkowitz.net (2001), http://www.perkowitz.net/research/papers/phd.ps.gz

  48. Perkowitz, M., Etzioni, O.: Towards adaptive web sites: Conceptual framework and case study. Artificial Intelligence (2000), http://linkinghub.elsevier.com/retrieve/pii/S0004370299000983

  49. Perugini, S., Goncalves, M., Fox, E.: Recommender systems research: A connection-centric survey (2004), http://apps.isiknowledge.com/InboundService.do?Func=Frame&product=WOS&action=retrieve&SrcApp=Papers&UT=000223535400001&SID=1BbJAfa4gIa8KKFIpkl&Init=Yes&SrcAuth=mekentosj&mode=FullRecord&customersID=mekentosj&DestFail=http%253A%252F%252Faccess.isiproducts.com%252Fcustom_images%252Fwok_failed_auth.html

  50. Pierrakos, D., Paliouras, G., Papatheodorou, C.: Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction (2003), http://www.springerlink.com/index/X0T6WPPW58883587.pdf

  51. Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.: Web usage mining as a tool for personalization: A survey. User Modeling and User-Adapted Interaction 13(4), 311–372 (2003)

    Article  Google Scholar 

  52. Plaskoff, J.: Intersubjectivity and community building: Learning to learn organizationally, pp. 161–184 (2003)

    Google Scholar 

  53. Preece, J.: Sociability and usability in online communities: determining and measuring success. Behaviour & Information Technology (2001), http://www.informaworld.com/index/M9EMFTN4DGR0DAPA.pdf

  54. Preece, J.: Etiquette, empathy and trust in communities of practice: Stepping-stones to social capital. Journal of Universal Computer Science (2004), http://www.jucs.org/jucs_10_3/etiquette_empathy_and_trust/Preece_J.html

  55. Preece, J., Maloney-Krichmar, D.: Online communities: Focusing on sociability and usability. In: Handbook of Human-Computer Interaction (2003), http://isis.ku.dk/kurser/blob.aspx?feltid=102191

  56. Probst, G., Borzillo, S.: Why communities of practice succeed and why they fail. European Management Journal 26(5), 335–347 (2008), http://www.sciencedirect.com/science/article/B6V9T-4SWP24X-1/2/dbb451298682776766f494b7e25154e6

    Article  Google Scholar 

  57. Rheingold, H.: A slice of my life in my virtual community. Global networks: Computers and international communication, 57–80 (1993), http://books.google.com/books?hl=en&lr=&id=xI_Um3dTTeYC&oi=fnd&pg=PA413&dq=A+Slice+of+Life+in+My+Virtual+Community&ots=iUN7YtlQ8n&sig=peOI5xd3N6hGvmFM-h2obW9jf6A

  58. Ríos, S.A.: A study on web mining techniques for off-line enhancements of web sites. Ph.D Thesis p. 231 (2007)

    Google Scholar 

  59. Ríos, S.A., Aguilera, F., Guerrero, L.: Virtual communities of practice’s purpose evolution analysis using a concept-based mining approach. Knowledge-Based and Intelligent Information and Engineering Systems 2, 480–489 (2009)

    Article  Google Scholar 

  60. Ríos, S.A., Velásquez, J.D.: Semantic web usage mining by a concept-based approach for off-line web site enhancements. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 234–241 (2008), doi:10.1109/WIIAT.2008.406, http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4740455&isnumber=4740405&punumber=4740404&k2dockey=4740455ieeecnfs

  61. Ríos, S.A., Velásquez, J.D., Vera, E.S., Yasuda, H., Aoki, T.: Using sofm to improve web site text content. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 622–626. Springer, Heidelberg (2005), http://www.springerlink.com/index/dmt1u7rld84cv2mr.pdf

    Chapter  Google Scholar 

  62. Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T.: Web site improvements based on representative pages identification. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1162–1166. Springer, Heidelberg (2005), http://www.springerlink.com/index/pp1125r774w3358m.pdf

    Chapter  Google Scholar 

  63. Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T.: Conceptual classification to improve a web site content. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 869–877. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  64. Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T.: A hybrid system for concept-based web usage mining. International Journal of Hybrid Intelligent Systems (IJHIS) 3(4), 219–235 (2006), http://iospress.metapress.com/index/6JB1PJ9VVF5F0TW0.pdf

    MATH  Google Scholar 

  65. Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T.: Using a self organizing feature map for extracting representative web pages from a web site. International Journal of Computational Intelligence Research (IJCIR) 2, 159–167 (2006), http://www.softcomputing.net/ijcir/1003a.pdf

    Google Scholar 

  66. Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T.: Web site off-line structure reconfiguration: A web user browsing analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 371–378. Springer, Heidelberg (2006), http://www.springerlink.com/content/k1147742h457/

    Chapter  Google Scholar 

  67. Rogers, E.M., Kincaid, D.L.: Communication networks: Toward a new paradigm for research, p. 386, http://www.amazon.com/Communication-Networks-Toward-Paradigm-Research/dp/0029267404

  68. Scime, A.: Web mining: applications and techniques? p. 427 (2005), http://books.google.com/books?id=TDhPMs3adw0C&printsec=frontcover

  69. Scott, J.: Social network analysis: a handbook, p. 208 (2000), http://books.google.com/books?id=Ww3_bKcz6kgC&printsec=frontcover

  70. Shummer, T.: Patterns for building communities in collaborative systems. In: Proceedings of the 9th European Conference on Pattern Languages and Programs (2004), http://hillside.net/europlop/europlop2004/Papers/wwc/C5.pdf

  71. Spiliopoulou, M.: Web usage mining for web site evaluation. Communications of the ACM (2000), http://portal.acm.org/citation.cfm?doid=345124.345167

  72. Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A framework for the evaluation of session reconstruction heuristics in web-usage analysis. INFORMS Journal on Computing (2003), http://warhol.wiwi.hu-berlin.de/~berendt/Papers/spiliopoulou_etal_2003.pdf

  73. Srivastava, J., Cooley, R., Deshpande, M., Tan, P.: Web usage mining: discovery and applications of usage patterns from web data. In: ACM SIGKDD Explorations Newsletter (2000), http://portal.acm.org/citation.cfm?id=846188&dl=GUIDE

  74. Sudweeks, F., Simoff, S.J.: Complementary explorative data analysis: the reconciliation of quantitative and qualitative principles. Doing Internet Research: Critical Issues and Methods for Examining the Net, 29–55 (1999)

    Google Scholar 

  75. Tao, Y., Hong, T., Su, Y.: Web usage mining with intentional browsing data. Expert Systems With Applications (2008), http://linkinghub.elsevier.com/retrieve/pii/S0957417407000668

  76. Turney, P.: Mining the web for lexical knowledge to improve keyphrase extraction: Learning from labeled and unlabeled data. Arxiv preprint cs.LG (2002), https://iit-iti.nrc-cnrc.gc.ca/iit-publications-iti/docs/NRC-44947.pdf

  77. Wasserman, S., Faust, K.: Social network analysis: Methods and applications. books.google.com (1994), http://books.google.com/books?hl=en&lr=&id=CAm2DpIqRUIC&oi=fnd&pg=PR21&dq=clustering+social+networks&ots=HtMntfXEPe&sig=VaA_-1yEkg_euBpXsJOCcnCclRk

  78. Wellman, B.: An electronic group is virtually a social network. Culture of the Internet (1997), http://books.google.com/books?hl=en&lr=&id=5uarXm1CkccC&oi=fnd&pg=PA179&dq=%2522An+Electronic+Group+is+Virtually+a+Social+Network%2522&ots=KS6xYR3g4S&sig=kl-Gh63xd_Qsg_MrUOGMYez4pDg

  79. Wellman, B.: Changing connectivity: A future history of y2. 03k. socresonline.org.uk (2000), http://www.socresonline.org.uk/cgi-bin/perlfect/search/search.pl?q=chris&showurl=%252F4%252F4%252Fwellman.html

  80. Wellman, B.: Computer networks as social networks. Science 293, 2031–2035 (2001), http://adsabs.harvard.edu/abs/2001Sci...293.2031W

    Article  Google Scholar 

  81. Wellman, B., Berkowitz, S.D.: Social structures: A network approach, vol. 15, p. 528. Emerald Group Publishing Limited (1998)

    Google Scholar 

  82. Wellman, B., Gulia, M.: Virtual communities as communities. Communities in cyberspace (1999), http://books.google.com/books?hl=en&lr=&id=harO_jeoyUwC&oi=fn%d&pg=PA167&dq=%2522Virtual+communities+as+communities%2522&ots=JWUI8GaxsS&sig=FIcftIzZwMS1GfCHTxokSxFdjBg

  83. Wellman, B., Salaff, J., Dimitrova, D., Garton, L.: Computer networks as social networks: Collaborative work, telework, and virtual community. Annual Reviews in Sociology (1996), http://arjournals.annualreviews.org/doi/abs/10.1146%252Fannurev.soc.22.1.213

  84. Wenger, E.: Communities of practice: Learning, meaning, and identity. books.google.com (1999), http://books.google.com/books?hl=en&lr=&ie=UTF-8&id=heBZpgYUK%dAC&oi=fnd&pg=PR11&dq=%2522Wenger%2522&ots=kcmh-sbxZk&sig=8F_tXXNLuHe4r5FEE42t%cVcmoxU

  85. Wenger, E.: Communities of practice: Learning, meaning, and identity (1999)

    Google Scholar 

  86. Wenger, E., McDermott, R., Snyder, W.: Cultivating communities of practice: A guide to managing knowledge. Harvard Business School Press, Boston (2002), http://emergence.org/ECO_site/web-content/V8_Books/details/1095.html

  87. Wenger, E., McDermott, R.A., Snyder, W.: Cultivating communities of practice (2002)

    Google Scholar 

  88. Wenger, E., White, N., Smith, J.: Digital habitats; stewarding technology for communities. books.google.com (2010), http://books.google.com/books?hl=en&lr=&id=E7GPhmV4-KkC&oi=fnd&pg=PR11&dq=%2522Technology+for+communities%2522&ots=2BXXiV7Aux&sig=zH9dDtb-In4RU24FU9hYOt3AsfM

  89. Wenger, E., White, N., Smith, J., Rowe, K.: Technology for communities. CEFRIO Book Chapter (January 2005), http://waterwiki.net/images/9/97/Technology_for_communities_-_book_chapter.pdf

  90. Xu, J., Chen, H.: Crimenet explorer: a framework for criminal network knowledge discovery. In: ACM Transactions on Information Systems, TOIS (2005), http://portal.acm.org/citation.cfm?id=1059984 (Aqui sale bien el blockmodeling)

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Ríos, S.A., Aguilera, F. (2010). Web Intelligence on the Social Web. In: Velásquez, J.D., Jain, L.C. (eds) Advanced Techniques in Web Intelligence - I. Studies in Computational Intelligence, vol 311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14461-5_9

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  • DOI: https://doi.org/10.1007/978-3-642-14461-5_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14460-8

  • Online ISBN: 978-3-642-14461-5

  • eBook Packages: EngineeringEngineering (R0)

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