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Discovery of Web user communities and their role in personalization

  • Georgios Paliouras
Original Paper
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Abstract

One of the major innovations in personalization in the last 20 years was the injection of social knowledge into the model of the user. The user is not considered an isolated individual any more, but a member of one or more communities. User communities have been facilitated by the striking advancements of electronic communications and in particular the penetration of the Web into people’s everyday routine. Communities arise in a number of different ways. Social networking tools typically allow users to proactively connect to each other. Alternatively, data mining tools discover communities of connected Web sites or communities of Web users. In this article, we focus on the latter type of community, which is commonly mined from logs of users’ activity on the Web. We recall how this process has been used to model the users’ interests and personalize Web applications. Collaborative filtering and recommendation are the most widely used forms of community-driven personalization. However, we examine a range of other interesting alternatives that are worth investigating further. This effort leads us naturally to the recent developments on the Web and particularly the advent of the social Web. We explain how this development draws together the different viewpoints on Web communities and introduces new opportunities for community-based personalization. In particular, we propose the concept of active user community and show how this relates to recent efforts on mining social networks and social media.

Keywords

User communities Web mining Web personalization Web communities Social networks 
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References

  1. Adamic L.A., Adar E.: Friends and neighbors on the web. Soc. Netw. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  2. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, pp. 207–216 (1993)Google Scholar
  3. Almeida, R.B., Almeida, V.A.F.: A community-aware search engine. In: Feldman, S.I., Uretsky, M., Najork, M., Wills, C.E. (eds.) Proceedings of the Thirteenth International Conference on World Wide Web (WWW), New York, pp. 413–421 (2004)Google Scholar
  4. Anaya, A.R., Boticario, J.: Clustering learners according to their collaboration. In: Borges, M.R.S., Shen, W., Pino, J.A., Barthès, J.P.A., Luo, J., Ochoa, S.F., Yong, J. (eds.) Proceedings of the 13th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Santiago, pp. 540–545 (2009)Google Scholar
  5. Ardissono, L., Kuflik, T., Petrelli, D.: Personalization in cultural heritage: the road travelled and the one ahead. User Model. User-Adap. Inter. 22(1–2), 73–99 (2012)Google Scholar
  6. Baeza-Yates R.A., Ribeiro-Neto B.A.: Modern Information Retrieval. ACM Press, Addison-Wesley (1999)Google Scholar
  7. Berners-Lee T.: Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web. Harper, San Francisco (2000)Google Scholar
  8. Blei D.M., Ng A.Y., Jordan M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  9. Bouras C., Igglesis V., Kapoulas V., Tsiatsos T.: A web-based virtual community. Int. J. Web Based Comun. 1(2), 127–139 (2005)CrossRefGoogle Scholar
  10. Boyd, D.M., Ellison, N.B.: Social network sites: definition, history, and scholarship. J. Comput. Mediat. Commun. 13(1), 210–230 (2008)CrossRefGoogle Scholar
  11. Burke R.D.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W (eds) The Adaptive Web, Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321., pp. 377–408. Springer, Heidelberg (2007)Google Scholar
  12. Caragea, D., Bahirwani, V., Aljandal, W., Hsu, W.H.: Ontology-based link prediction in the livejournal social network. In: Bulitko, V., Beck, J.C. (eds.) Proceedings of the Eighth Symposium on Abstraction, Reformulation, and Approximation (SARA), Lake Arrowhead (2009)Google Scholar
  13. Chen W., Liu Z., Sun X., Wang Y.: A game-theoretic framework to identify overlapping communities in social networks. Data Min. Knowl. Discov. 21(2), 224–240 (2010)MathSciNetCrossRefGoogle Scholar
  14. Cooley, R., Mobasher, B., Srivastava, J.: Web mining: information and pattern discovery on the world wide web. In: Proceedings of the Ninth International Conference on Tools with Artificial Intelligence (ICTAI), Newport Beach, pp. 558–567 (1997)Google Scholar
  15. Cooley R., Mobasher B., Srivastava J.: Data preparation for mining world wide web browsing patterns. Knowl. Inform. Syst. 1(1), 5–32 (1999)Google Scholar
  16. Cui H., Wen J.R., Nie J.Y., Ma W.Y.: Query expansion by mining user logs. IEEE Tran. Knowl. Data Eng. 15(4), 829–839 (2003)CrossRefGoogle Scholar
  17. Desmarais, M.C., Baker, R.S.J.d.: A review of recent advances in learner and skill modeling in intelligent learning environments. User Model. User-Adap. Inter. 22(1–2), 9–38 (2012)Google Scholar
  18. Du N., Wang B., Wu B.: Community detection in complex networks. J. Comput. Sci. Technol. 23(4), 672–683 (2008)MathSciNetCrossRefGoogle Scholar
  19. Farzan R., Brusilovsky P: Social navigation support in a course recommendation system. In: Wade, V.P., Ashman, H., Smyth, B (eds) Adaptive Hypermedia and Adaptive Web-Based Systems, Proceedings of the Fourth International Conference (AH). Lecture Notes in Computer Science, vol. 4018, pp. 91–100. Springer, Dublin (2006)Google Scholar
  20. Fink J., Kobsa A.: User modeling for personalized city tours. Artif. Intell. Rev. 18(1), 33–74 (2002)MATHCrossRefGoogle Scholar
  21. Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Boston, pp. 150–160 (2000)Google Scholar
  22. Fortunato S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  23. Gaudioso E., Boticario J.: User modeling on adaptive web-based learning communities. In: Palade, V., Howlett, R.J., Jain, L.C (eds) Proceedings of the Seventh International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES). Lecture Notes in Computer Science, vol. 2774, pp. 260–266. Springer, Oxford (2003)Google Scholar
  24. George, T., Merugu, S.: A scalable collaborative filtering framework based on co-clustering. In: Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM), Houston, pp. 625–628 (2005)Google Scholar
  25. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Philadelphia, pp. 230–237 (1999)Google Scholar
  26. Hill, W.C., Stead, L., Rosenstein, M., Furnas, G.W.: Recommending and evaluating choices in a virtual community of use. In: Adelson, B., Dumais, S.T., Olson, J.S. (eds.) Proceedings of the Conference on Human Factors in Computing Systems (CHI), Denver, pp. 194–201 (1995)Google Scholar
  27. Hofmann, T.: Probabilistic latent semantic analysis. In: Laskey, K.B., Prade, H. (eds.) Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI), Stockholm, pp. 289–296 (1999)Google Scholar
  28. Hofmann T.: Latent semantic models for collaborative filtering. ACM Trans. Inform. Syst. 22(1), 89–115 (2004)CrossRefGoogle Scholar
  29. Jäschke, R., Marinho, L.B., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in folksonomies. In: Kok, J.N., Koronacki, J., deMántaras, R.L., Matwin, S., Mladenic, D., Skowron, A. (eds.) Proceedings of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). Lecture Notes in Computer Science, vol. 4702, pp. 506–514. Springer, Warsaw (2007)Google Scholar
  30. Jin, X., Zhou, Y., Mobasher, B.: Web usage mining based on probabilistic latent semantic analysis. In: Kim, W., Kohavi, R., Gehrke, J., DuMouchel, W. (eds.) Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Seattle, pp. 197–205 (2004)Google Scholar
  31. Joachims, T., Freitag, D., Mitchell, T.M.: Web watcher: a tour guide for the world wide web. In: Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), Nagoya, vol. 1, pp. 770–777 (1997)Google Scholar
  32. Kashoob, S., Caverlee, J., Kamath, K.: Community-based ranking of the social web. In: Chignell, M.H., Toms, E. (eds.) Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (HT), Toronto, pp. 141–150 (2010)Google Scholar
  33. Kim, J.: User-generated content (ugc) revolution?: critique of the promise of youtube. Ph.D. thesis, University of Iowa (2010)Google Scholar
  34. Kim S., Fox E.A.: Interest-based user grouping model for collaborative filtering in digital libraries. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E.A., Lim, E.P (eds) Digital Libraries: International Collaboration and Cross-Fertilization, Proceedings of the Seventh International Conference on Asian Digital Libraries (ICADL). Lecture Notes in Computer Science, vol. 3334, pp. 533–542. Springer, Shanghai (2004)Google Scholar
  35. Kittur, A., Suh, B., Pendleton, B.A., Chi, E.H., Suh, B., Mytkowicz, T.: Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie. In: Presented at alt.CHI at ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), San Jose, pp. 453–462 (2007)Google Scholar
  36. Kohrs, A., Mérialdo, B.: Clustering for collaborative filtering applications. In: Proceedings of Computational Intelligence for Modelling, Control and Automation, Vienna (1999)Google Scholar
  37. Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-Adap. Inter. 22(1–2), 101–123 (2012)Google Scholar
  38. Konstan J.A., Miller B.N., Maltz D., Herlocker J.L., Gordon L.R., Riedl J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  39. Kosala R., Blockeel H.: Web mining research: a survey. SIGKDD Explor. 2(1), 1–15 (2000)CrossRefGoogle Scholar
  40. Kumar R., Raghavan P., Rajagopalan S., Tomkins A.: Trawling the web for emerging cyber-communities. Comput. Netw. 31(11-16), 1481–1493 (1999)CrossRefGoogle Scholar
  41. Lin, Y.R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.L.: Blog community discovery and evolution based on mutual awareness expansion. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, pp. 48–56 (2007)Google Scholar
  42. Linden G., Smith B., York J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  43. Marlin B.: Modeling user rating profiles for collaborative filtering. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds) Advances in Neural Information Processing Systems (NIPS), Vancouver, British Columbia (2003)Google Scholar
  44. McDaid, A., Hurley, N.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: N. Memon, R. Alhajj (eds.) Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 112–119. Odense, Denmark (2010)Google Scholar
  45. Middleton S.E., Roure D.D., Shadbolt N.R.: Ontology-based recommender systems. In: Staab, S., Studer, R. (eds) Handbook on Ontologies, International Handbooks on Information Systems, pp. 779–796. Springer, Heidelberg (2009)Google Scholar
  46. Mitrovic, A.: Fifteen years of constraint-based tutors: what we have achieved and where we are going. User Model. User-Adap. Inter. 22(1–2), 39–72 (2012)Google Scholar
  47. Mobasher, B.: Data mining for web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, Methods and Strategies of Web Personalization. Lecture Notes in Computer Science, vol. 4321, pp. 90–135. Springer, Heidelberg (2007)Google Scholar
  48. Mobasher B., Cooley R., Srivastava J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000)CrossRefGoogle Scholar
  49. Mobasher B., Dai H., Luo T., Nakagawa M.: Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowl. Discov. 6(1), 61–82 (2002)MathSciNetCrossRefGoogle Scholar
  50. Mobasher, B., Jin, X., Zhou, Y.: Semantically enhanced collaborative filtering on the web. In: Berendt, B., Hotho, A., Mladenic, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) Web Mining: From Web to Semantic Web, Revised Selected and Invited Papers of the First European Web Mining Forum, EMWF. Lecture Notes in Computer Science, vol. 3209, pp. 57–76. Springer, Heidelberg (2004)Google Scholar
  51. Naphade M.R., Smith J.R., Tesic J., Chang S.F., Hsu W.H., Kennedy L.S., Hauptmann A.G., Curtis J.: Large-scale concept ontology for multimedia. IEEE MultiMed. 13(3), 86–91 (2006)CrossRefGoogle Scholar
  52. Nasraoui O., Frigui H., Krishnapuram R., Joshi A.: Extracting web user profiles using relational competitive fuzzy clustering. Int. J. Artif. Intell. Tools 9(4), 509–526 (2000)CrossRefGoogle Scholar
  53. Nazir, A., Raza, S., Chuah, C.N.: Unveiling facebook: a measurement study of social network based applications. In: Papagiannaki, K., Zhang, Z.L. (eds.) Proceedings of the Eighth ACM SIGCOMM Conference on Internet Measurement, Vouliagmeni, pp. 43–56 (2008)Google Scholar
  54. O’Connor, M., Herlocker, J.L.: Clustering items for collaborative filtering. In: Proceedings of the ACM SIGIR Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley (1999)Google Scholar
  55. Orwant J.: Heterogeneous learning in the doppelgänger user modeling system. User Model. User-Adap. Inter. 4(2), 107–130 (1995)CrossRefGoogle Scholar
  56. Paliouras, G., Papatheodorou, C., Karkaletsis, V., Spyropoulos, C.D.: Clustering the users of large web sites into communities. In: Langley, P. (ed.) Proceedings of the Seventeenth International Conference on Machine Learning (ICML), Stanford, pp. 719–726 (2000)Google Scholar
  57. Parra, D., Brusilovsky, P.: Collaborative filtering for social tagging systems: an experiment with citeulike. In: Bergman, L.D., Tuzhilin, A., Burke, R.D., Felfernig, A., Schmidt-Thieme, L. (eds.) Proceedings of the ACM Conference on Recommender Systems (RecSys), New York, pp. 237–240 (2009)Google Scholar
  58. Pathak, N., DeLong, C., Banerjee, A., Erickson, K.: Social topic models for community extraction. In: Proceedings of the Second International Workshop on Advances in Social Network Mining and Analysis (SNAKDD), Las Vegas, pp. 77–96 (2008)Google Scholar
  59. Pazzani, M.J., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference (AAAI/IAAI), Portland, vol. 1, pp. 54–61 (1996)Google Scholar
  60. Perkowitz M., Etzioni O.: Towards adaptive web sites: conceptual framework and case study. Artif. Intell. 118(1-2), 245–275 (2000)MATHCrossRefGoogle Scholar
  61. Pierrakos D., Paliouras G.: Personalizing web directories with the aid of web usage data. IEEE Trans. Knowl. Data Eng. 22(9), 1331–1344 (2010)CrossRefGoogle Scholar
  62. Pierrakos D., Paliouras G., Papatheodorou C., Spyropoulos C.D.: Web usage mining as a tool for personalization: a survey. User Model. User-Adap. Inter. 13(4), 311–372 (2003)CrossRefGoogle Scholar
  63. Rheingold H.: The Virtual Community: Homesteading on the Electronic Frontier. Addison-Wesley, New York (1993)Google Scholar
  64. Rich E.: User modeling via stereotypes. Cogn. Sci. 3(4), 329–354 (1979)CrossRefGoogle Scholar
  65. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Application of dimensionality reduction in recommender system—a case study. Technical Report CS-TR 00-043, Computer Science and Engineering Department, University of Minnesota (2000)Google Scholar
  66. Schafer J.B., Konstan J.A., Riedl J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5, 115–153 (2001)MATHCrossRefGoogle Scholar
  67. Schroedl, S., Kesari, A., Neumeyer, L.: Personalized ad placement in web search. In: Proceedings of the 4th Annual International Workshop on Data Mining and Audience Intelligence for Online Advertising (AdKDD), Washington USA (2010)Google Scholar
  68. Schuler D.: Community networks: building a new participatory medium. Commun. ACM 37(1), 38–51 (1994)CrossRefGoogle Scholar
  69. Seth A., Zhang J., Cohen R: Bayesian credibility modeling for personalized recommendation in participatory media. In: Bra, P.D., Kobsa, A., Chin, D.N (eds) Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization (UMAP) Lecture Notes in Computer Science, vol 6075., pp. 279–290. Springer, Big Island (2010)Google Scholar
  70. Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Katz, I.R., Mack, R.L., Marks, L., Rosson, M.B., Nielsen, J. (eds.) Proceedings of the Conference on Human Factors in Computing Systems (CHI), Denver, pp. 210–217 (1995)Google Scholar
  71. Siersdorfer, S., Sizov, S.: Social recommender systems for web 2.0 folksonomies. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (HYPERTEXT), Torino, pp. 261–270 (2009)Google Scholar
  72. Smyth B.: A community-based approach to personalizing web search. IEEE Comput. 40(8), 42–50 (2007)CrossRefGoogle Scholar
  73. Snoek, C., Worring, M., van Gemert, J., Geusebroek, J.M., Smeulders, A.W.M.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Nahrstedt, K., Turk, M., Rui, Y., Klas, W., Mayer-Patel, K. (eds.) Proceedings of the 14th ACM International Conference on Multimedia, Santa Barbara, pp. 421–430 (2006)Google Scholar
  74. Spiliopoulou, M., Faulstich, L.: WUM—a tool for www ulitization analysis. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) Selected Papers of the International Workshop on World Wide Web and Databases (WebDB). Lecture Notes in Computer Science, vol. 1590, pp. 184–103. Springer, Heidelberg (1998)Google Scholar
  75. Srivastava J., Cooley R., Deshpande M., Tan P.N.: Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. 1(2), 12–23 (2000)CrossRefGoogle Scholar
  76. Staab S., Angele J., Decker S., Erdmann M., Hotho A., Maedche A., Schnurr H.P., Studer R., Sure Y.: Semantic community web portals. Comput. Netw. 33(1-6), 473–491 (2000)CrossRefGoogle Scholar
  77. Stock O., Zancanaro M., Busetta P., Callaway C., Kruger A., Kruppa M., Kuflik T., Not E., Rocchi C.: Adaptive, intelligent presentation of information for the museum visitor in peach. User Model. User-Adap. Inter. 17, 257–304 (2007)CrossRefGoogle Scholar
  78. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 19p. doi: 10.1155/2009/421425 (2009)
  79. Sun J., Tsourakakis C.E., Hoke E., Faloutsos C., Eliassi-Rad T.: Two heads better than one: pattern discovery in time-evolving multi-aspect data. Data Min. Knowl. Discov. 17(1), 111–128 (2008)MathSciNetCrossRefGoogle Scholar
  80. Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Pu, P., Bridge, D.G., Mobasher, B., Ricci, F. (eds.) Proceedings of the ACM Conference on Recommender Systems (RecSys), Lausanne, pp. 43–50 (2008)Google Scholar
  81. Tao, X., Li, Y., Zhong, N., Nayak, R.: Ontology mining for personalized web information gathering. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Silicon Valley, pp. 351–358 (2007)Google Scholar
  82. Toch, E., Wang, Y., Cranor, L.F.: Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. User Model. User-Adap. Inter. 22(1–2), 203–220 (2012)Google Scholar
  83. Ungar, L.H., Foster, D.P.: Clustering methods for collaborative filtering. In: Proceedings of the Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence, Madison (1998)Google Scholar
  84. van Hage, W.R., Stash, N., Wang, Y., Aroyo, L.: Finding your way through the Rijksmuseum with an adaptive mobile museum guide. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) The Semantic Web: Research and Applications, Proceedings of the Seventh Extended Semantic Web Conference (ESWC), Part I. Lecture Notes in Computer Science, vol. 6088, pp. 46–59. Springer, Heraklion (2010)Google Scholar
  85. Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: Proceedings of the International Conference on Service Systems and Service Management, Tokyo, pp. 1–5 (2007)Google Scholar
  86. Wu K.L., Yu P.S., Ballman A.: Speedtracer: a web usage mining and analysis tool. IBM Syst. J. 37(1), 89–105 (1998)CrossRefGoogle Scholar
  87. Xu Z., Tresp V., Rettinger A., Kersting K.: Social network mining with nonparametric relational models. In: Giles, C.L., Smith, M., Yen, J., Zhang, H (eds) Revised Selected Papers of the Second International Workshop on Advances in Social Network Mining and Analysis (SNAKDD) Lecture Notes in Computer Science, vol 5498., pp. 77–96. Springer, Heidelberg (2010)Google Scholar
  88. Yan T.W., Jacobsen M., Garcia-Molina H., Dayal U.: From user access patterns to dynamic hypertext linking. Comput. Netw. 28(7–11), 1007–1014 (1996)Google Scholar
  89. Zhou, Y., Davis, J.: Discovering web communities in the blogspace. In: Proceedings of the 40th Hawaii International Conference on Systems Science (HICSS), Waikoloa, p. 85 (2007)Google Scholar
  90. Zhuge H.: Communities and emerging semantics in semantic link network: discovery and learning. IEEE Trans. Knowl. Data Eng. 21(6), 785–799 (2009)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute of Informatics and TelecommunicationsNational Centre for Scientific Research “Demokritos”AttikiGreece

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