Advertisement

Analysis of Social Networks Extracted from Log Files

  • Kateřina SlaninováEmail author
  • Jan Martinovič
  • Pavla Dráždilová
  • Gamila Obadi
  • Václav Snášel
Chapter

Abstract

Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear online at www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the “starred” version of the new Springer abstract command for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstract command if the abstract is also to appear in the printed version of the book.

Keywords

Social Network Singular Value Decomposition Social Network Analysis Pattern Mining Spectral Cluster 
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

The authors acknowledge the support of the following projects: SP/ 2010196 – Machine Intelligence and SGS/24/2010 – The Usage of BI and BPM Systems to Efficiency Management Support.

References

  1. 1.
    Cláudia M. Antunes and Arlindo L. Oliveira. Grammatical Inference: Algorithms and Applications, volume 2484, chapter Inference of Sequential Association Rules Guided by Context-Free Grammars, pages 289–293. Springer, Berlin, 2002Google Scholar
  2. 2.
    Alex Arenas, Alberto Fernandez, and Sergio Gomez. Analysis of the structure of complex networks at different resolution levels. New J. Phys., 10, 2008Google Scholar
  3. 3.
    Sitaram Asur, Srinivasan Parthasarathy, and Duygu Ucar. An event-based framework for characterizing the evolutionary behavior of interaction graphs. In KDD ’07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 913–921, NY, 2007. ACMGoogle Scholar
  4. 4.
    Daniel Barbar, Julia Couto, Sushil Jajodia, Leonard Popyack, and Ningning Wu. Adam: Detecting intrusions by data mining. In In Proceedings of the IEEE Workshop on Information Assurance and Security, pages 11–16, 2001Google Scholar
  5. 5.
    G. Benoît. Data mining. Annual Review of Information Science and Technology, 36:265–310, 2002CrossRefGoogle Scholar
  6. 6.
    P. Berkhin. A survey of clustering data mining techniques. In Grouping Multidimensional Data, pages 25–71. Springer, Berlin, 2006Google Scholar
  7. 7.
    James C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer, MA, 1981zbMATHGoogle Scholar
  8. 8.
    Mohamed Bouguessa, Benoît Dumoulin, and Shengrui Wang. Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In KDD ’08: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 866–874, NY, 2008. ACMGoogle Scholar
  9. 9.
    Ulrik Brandes, Daniel Delling, Marco Gaertler, Robert G?rke, Martin Hoefer, Zoran Nikoloski, and Dorothea Wagner. On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20:172–188, 2008Google Scholar
  10. 10.
    Peter Brusilovsky and Christoph Peylo. Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Ed., 13(2–4):159–172, 2003Google Scholar
  11. 11.
    F. Castro, A. Vellido, A. Nebot, and F. Mugica. Applying data mining techniques to e-learning problems. Studies in Computational Intelligence (SCI), 62:183–221, 2007Google Scholar
  12. 12.
    Deepayan Chakrabarti, Ravi Kumar, and Andrew Tomkins. Evolutionary clustering. In KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 554–560, NY, 2006. ACMGoogle Scholar
  13. 13.
    Anurat Chapanond, Mukkai S. Krishnamoorthy, and Bülent Yener. Graph theoretic and spectral analysis of enron email data. Comput. Math. Organ. Theory, 11(3):265–281, 2005Google Scholar
  14. 14.
    David Cheng, Ravi Kannan, Santosh Vempala, and Grant Wang. On a recursive spectral algorithm for clustering from pairwise similarities. Technical report, MIT, 2003Google Scholar
  15. 15.
    David Cheng, Ravi Kannan, Santosh Vempala, and Grant Wang. A divide-and-merge methodology for clustering. ACM Trans. Database Syst., 31(4):1499–1525, 2006Google Scholar
  16. 16.
    Hichang Cho, Geri Gay, Barry Davidson, and Anthony Ingraffea. Social networks, communication styles, and learning performance in a cscl community. Computers & Education, 49(2):309–329, 2007CrossRefGoogle Scholar
  17. 17.
    Aaron Clauset, M. E. J. Newman, and Cristopher Moore. Finding community structure in very large networks. Phys. Rev. E, 70(6):066111, 2004Google Scholar
  18. 18.
    Ram Dantu and Prakash Kolan. Detecting spam in voip networks. In SRUTI’05: Proceedings of the Steps to Reducing Unwanted Traffic on the Internet on Steps to Reducing Unwanted Traffic on the Internet Workshop, page 5, Berkeley, CA, USA, 2005. USENIX AssociationGoogle Scholar
  19. 19.
    Thanasis Daradoumis, Alejandra Martinez-Mones, and Fatos Xhafa. A layered framework for evaluating on-line collaborative learning interactions. International Journal of Human-Computer Studies, 64(7):622–635, 2006CrossRefGoogle Scholar
  20. 20.
    Anirban Dasgupta, John Hopcroft, Ravi Kannan, and Pradipta Mitra. Spectral clustering by recursive partitioning. In ESA’06: Proceedings of the 14th conference on Annual European Symposium, pages 256–267. Springer, Berlin, 2006Google Scholar
  21. 21.
    Supriya Kumar De and P. Radha Krishna. Clustering web transactions using rough approximation. Fuzzy Sets and Systems, 148(1):131–138, 2004Google Scholar
  22. 22.
    Imre Derényi, Gergely Palla, and Tamás Vicsek. Clique percolation in random networks. Physical Review Letters, 94(16), 2005Google Scholar
  23. 23.
    Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Ming Gu, and Horst D. Simon. A min-max cut algorithm for graph partitioning and data clustering. In ICDM ’01: Proceedings of the 2001 IEEE International Conference on Data Mining, pages 107–114, Washington, DC, USA, 2001. IEEE Computer SocietyGoogle Scholar
  24. 24.
    W. E. Donath and A. J. Hoffman. Lower bounds for the partitioning of graphs. IBM J. Res. Dev., 17(5):420–425, 1973zbMATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    G. M. Downs and J. M. Barnard. Reviews in Computational Chemistry. Wiley, NY, 2003Google Scholar
  26. 26.
    Pavla Dráždilová, Gamila Obadi, Kateřina Slaninová, Shawki Al-Dubaee, Jan Martinovič, and Václav Snášel. Computational intelligence methods for data analysis and mining of eLearning activities. In Xhafa, F., Caballe, S., Abraham, A., Daradoumis, T., Juan Perez, A. A., editors, Computational Intelligence For Technology Enhanced Learning, pages 195–224. Springer-Verlag, Heidelberg, 2010Google Scholar
  27. 27.
    Pavla Dráždilová, Gamila Obadi, Kateřina Slaninová, Jan Martinovič, and Václav Snášel. Analysis and visualization of relations in elearning. In Computational Social Network Analysis. Springer, London, 2010Google Scholar
  28. 28.
    Pavla Dráždilová, Kateřina Slaninová, Jan Martinovič, Gamila Obadi, and Václav Snášel. Creation of students’ activities from learning management system and their analysis. In CASoN, pages 155–160, 2009Google Scholar
  29. 29.
    Pavla Drázdilová, Katerina Slaninová, Jan Martinovic, Gamila Obadi, and Václav Snásel. Creation of students’ activities from learning management system and their analysis. In CASoN, pages 155–160, 2009Google Scholar
  30. 30.
    Margaret H. Dunham. Data Mining: Introductory and Advanced Topics. Prentice Hall, NJ, 2003Google Scholar
  31. 31.
    Peter Ekler, Zoltan Ivanfi, and Kristof Aczel. Similarity management in phonebook-centric social networks. In ICIW ’09: Proceedings of the 2009 Fourth International Conference on Internet and Web Applications and Services, pages 273–279, Washington, DC, 2009. IEEE Computer SocietyGoogle Scholar
  32. 32.
    Lars Eldén. Matrix Methods in Data Mining and Pattern Recognition. Society for Industrial and Applied Mathematics, Philadelphia, PA, 2007zbMATHGoogle Scholar
  33. 33.
    C. Faloutsos. Searching multimedia databases by content. IEEE Data Eng. Bull., 4(18):31–40, 1995Google Scholar
  34. 34.
    Li Ma Fang Wei, Chen Wang and Aoying Zhou. Detecting overlapping community structures in networks with global partition and local expansion. In Progress in WWW Research and Development, pages 43–55. Springer, Berlin, 2008Google Scholar
  35. 35.
    K Fansler and R Riegle. A model of online instructional design analytics. In Proceedings of the 20th Annual Conference on Distance Teaching and Learning, 2005Google Scholar
  36. 36.
    Illés Farkas, Dániel Ábel, Gergely Palla, and Tamás Vicsek. Weighted network modules. New Journal of Physics, 9(6):180, 2007Google Scholar
  37. 37.
    Shelly Farnham, Sean Uberoi Kelly, Will Portnoy, and Jordan L. K. Schwartz. Wallop: Designing social software for co-located social networks. In HICSS ’04: Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS’04) – Track 4, page 40107.1, Washington, DC, USA, 2004. IEEE Computer SocietyGoogle Scholar
  38. 38.
    Daniel J. Fenn, Mason A. Porter, Mark McDonald, Stacy Williams, Neil F. Johnson, and Nick S. Jones. Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007-2008 credit crisis. Chaos, 19, 2009Google Scholar
  39. 39.
    M. Fiedler. Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23:298–305, 1973MathSciNetGoogle Scholar
  40. 40.
    Danyel Fisher and Paul Dourish. Social and temporal structures in everyday collaboration. In CHI ’04: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 551–558, NY, 2004. ACMGoogle Scholar
  41. 41.
    S. Fortunato. Community detection in graphs. arXiv:0906.0612, 2009Google Scholar
  42. 42.
    L. C. Freeman. The Development of Social Network Analysis: A Study in the Sociology of Science. Empirical Press, 2004Google Scholar
  43. 43.
    G. Gan, C. Ma, and J. Wu. Data clustering: Theory, algorithms, and applications. ASA-SIAM Series on Statistics and Applied Probability, 20:480, 2007MathSciNetGoogle Scholar
  44. 44.
    D. Gibson, J. Kleinberg, and P. Raghavan. Inferring web communities from link topology. In HYPERTEXT ’98: Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia: Links, Objects, Time and Space-Structure in Hypermedia Systems, pages 255–234, 1998Google Scholar
  45. 45.
    Gene H. Golub and Charles F. Van Loan. Matrix computations (3rd ed.). Johns Hopkins University Press, MD, 1996Google Scholar
  46. 46.
    Karl Groves. The limitations of server log files for usability analysis. http://www.boxesandarrows.com/view/the-limitations-of, 2007
  47. 47.
    W. Hämäläinen, T. H. Laine, and E. Sutinen. Data mining in personalizing distance education courses. In World Conference on Open Learning and Distance Education, pages 1–11, Hong Kong, 2004Google Scholar
  48. 48.
    W. Hämäläinen and M. Vinni. Comparison of machine learning methods for intelligent tutoring systems. In Proceedings of the Eighth International Conference in Intelligent Tutoring Systems, pages 525–534, 2006Google Scholar
  49. 49.
    David J. Hand, Padhraic Smyth, and Heikki Mannila. Principles of Data Mining. MIT, MA, 2001Google Scholar
  50. 50.
    Cesar A. Hidalgo and C. Rodriguez-Sickert. The dynamics of a mobile phone network. Physica A: Statistical Mechanics and its Applications, 387(12):3017–3024, 2008Google Scholar
  51. 51.
    Adel Hlaoui and Shengrui Wang. A direct approach to graph clustering. In Neural Networks and Computational Intelligence, pages 158–163, 2004Google Scholar
  52. 52.
    John Hopcroft, Omar Khan, Brian Kulis, and Bart Selman. Natural communities in large linked networks. In KDD ’03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 541–546, NY, 2003. ACMGoogle Scholar
  53. 53.
    Renta Ivncsy and Istvn Vajk. Frequent pattern mining in web log data. Acta Polytechnica Hungarica, Journal of Applied Science at Budapest Tech Hungary, Special Issue on Computational Intelligence, 3(1):77–90, 2006Google Scholar
  54. 54.
    A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: a review. ACM Comput. Surv., 31(3):264–323, 1999CrossRefGoogle Scholar
  55. 55.
    Anil K. Jain and Richard C. Dubes. Algorithms for Clustering Data. Prentice-Hall, NJ, 1988zbMATHGoogle Scholar
  56. 56.
    I.T. Jolliffe. Principal component analysis. In Springer Series in Statistics, page 487. Springerlink, NY, 2002Google Scholar
  57. 57.
    Ravi Kannan, Santosh Vempala, and Adrian Vetta. On clusterings: Good, bad and spectral. J. ACM, 51(3):497–515, 2004Google Scholar
  58. 58.
    N. Korfiatis, M. Poulos, and G. Bokos. Evaluating authoritative sources using social networks: An insight from wikipedia. Online Information Review, 30(3):252–262, 2006CrossRefGoogle Scholar
  59. 59.
    Beate Krause, Robert Jäschke, Andreas Hotho, and Gerd Stumme. Logsonomy - social information retrieval with logdata. In HT ’08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, pages 157–166, NY, 2008. ACMGoogle Scholar
  60. 60.
    R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins. Trawling the web for emerging cyber-communities. In WWW ’99: Proceedings of the Eighth International Conference on World Wide Web, pages 1481–1493, 1999Google Scholar
  61. 61.
    Jussi M. Kumpula, Mikko Kivelä, Kimmo Kaski, and Jari Saramäki. Sequential algorithm for fast clique percolation. Phys. Rev. E, 78(2):026109, 2008Google Scholar
  62. 62.
    Mikls Kurucz, Andrs A. Benczr, and Attila Pereszlnyi. Large-scale principal component analysis on livejournal friends network. In 2nd SNA-KDD Workshop ’08 (SNA-KDD’08), 2008Google Scholar
  63. 63.
    Laghos and Zaphiris. Sociology of student-centred e-learning communities: A network analysis. In IADIS international conference, Dublin, Ireland, July 2006. e-SocietyGoogle Scholar
  64. 64.
    R. Lambiotte, J. C. Delvenne, and M. Barahona. Dynamics and modular structure in networks. arXiv:0812.1770, 2008Google Scholar
  65. 65.
    Sang Hoon Lee, Pan-Jun Kim, Yong-Yeol Ahn, and Hawoong Jeong. Googling hidden interactions: Web search engine based weighted network construction, 2007Google Scholar
  66. 66.
    Lin Li, Shingo Otsuka, and Masaru Kitsuregawa. Query recommendation using large-scale web access logs and web page archive. In DEXA ’08: Proceedings of the 19th international conference on Database and Expert Systems Applications, pages 134–141, Berlin, 2008. SpringerGoogle Scholar
  67. 67.
    Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L. Tseng. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In WWW ’08: Proceeding of the 17th international conference on World Wide Web, pages 685–694, NY, 2008. ACMGoogle Scholar
  68. 68.
    Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L. Tseng. Analyzing communities and their evolutions in dynamic social networks. ACM Trans. Knowl. Discov. Data, 3(2):1–31, 2009Google Scholar
  69. 69.
    Leonid B. Litinskii and Dmitry E. Romanov. Neural network clustering based on distances between objects. In Artificial Neural Networks ICANN 2006, pages 437–443. Springer, Berlin, 2006Google Scholar
  70. 70.
    Ana Karla Alves De Medeiros, W.M.P. van der Aalst, and A J M M Weijters. Quantifying process equivalence based on observed behavior. Data Knowledge Engineering, 64:55–74, 2008Google Scholar
  71. 71.
    Bonnie A. Nardi, Steve Whittaker, Ellen Isaacs, Mike Creech, Jeff Johnson, and John Hainsworth. Integrating communication and information through contactmap. Commun. ACM, 45(4):89–95, 2002Google Scholar
  72. 72.
    Shyam Varan Nath. Crime pattern detection using data mining. In WI-IATW ’06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pages 41–44, Washington, DC, 2006. IEEE Computer SocietyGoogle Scholar
  73. 73.
    M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45:167–256, 2003zbMATHCrossRefMathSciNetGoogle Scholar
  74. 74.
    M. E. J. Newman and M. Girwan. Finding and evaluating community structure in networks. Physical Review E, 69(2):026113, 2004Google Scholar
  75. 75.
    Karthik Nilakant and Antonija Mitrovic. Applications of data mining in constraint-based intelligent tutoring systems. In Proceeding of the 2005 conference on Artificial Intelligence in Education, pages 896–898, Amsterdam, 2005. IOS PressGoogle Scholar
  76. 76.
    H. Ogata, Y. Yano, N. Furugori, and Q. Jin. Computer supported social networking for augmenting cooperation. Comput. Supported Coop. Work, 10(2):189–209, 2001CrossRefGoogle Scholar
  77. 77.
    J. P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A. L. Barabási. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences, 104(18):7332–7336, 2006CrossRefGoogle Scholar
  78. 78.
    Gergely Palla, Albert-László Barabási, and Tamás Vicsek. Quantifying social group evolution. Nature, 446:664–667, 2007CrossRefGoogle Scholar
  79. 79.
    Gergely Palla, Imre Derényi, Illés Farkas, and Tamás Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814–818, 2005CrossRefGoogle Scholar
  80. 80.
    Wei Peng, Tao Li, and Sheng Ma. Mining log files for data-driven system management. SIGKDD Explor. Newsl., 7(1):44–51, 2005Google Scholar
  81. 81.
    Santi Phithakkitnukoon and Ram Dantu. Inferring social groups using call logs. In OTM Workshops, pages 200–210, 2008Google Scholar
  82. 82.
    J. Pokorný, V. Snášel, and M. Kopecký. Dokumentografické informační systémy. Karolinum, 2005Google Scholar
  83. 83.
    Pascal Pons and Matthieu Latapy. Computing communities in large networks using random walks. In pInar Yolum, Tunga Güngör, Fikret Gürgen, and Can Özturan, editors, Computer and Information Sciences - ISCIS 2005, volume 3733, chapter 31, pages 284–293. Springer, Berlin, 2005Google Scholar
  84. 84.
    Mason A. Porter, Jukka-Pekka Onnela, and Peter J. Mucha. Communities in networks. Notices of the American Mathematical Society, 56:1082–1097, 2009zbMATHMathSciNetGoogle Scholar
  85. 85.
    Filippo Radicchi, Claudio Castellano, Federico Cecconi, Vittorio Loreto, and Domenico Parisi. Defining and identifying communities in networks, Feb 2004Google Scholar
  86. 86.
    Matthew J. Rattigan, Marc Maier, and David Jensen. Graph clustering with network structure indices. In ICML ’07: Proceedings of the 24th international conference on Machine learning, pages 783–790, NY, 2007. ACMGoogle Scholar
  87. 87.
    Joerg Reichardt and Stefan Bornholdt. Statistical mechanics of community detection, Mar 2006Google Scholar
  88. 88.
    Thomas Richardson, Peter J. Mucha, and Mason A. Porter. Spectral tripartitioning of networks. Physical Review E, 80(3):036111–036121, 2009CrossRefGoogle Scholar
  89. 89.
    C. Romero and S. Ventura. Educational data mining: A survey from 1995 - 2005. Expert Systems with Applications, 33:135–146, 2007CrossRefGoogle Scholar
  90. 90.
    Martin Rosvall and Carl T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118–1123, 2008CrossRefGoogle Scholar
  91. 91.
    A. Rozinat and W. M. P. van der Aalst. Conformance checking of processes based on monitoring real behavior. Inf. Syst., 33(1):64–95, 2008CrossRefGoogle Scholar
  92. 92.
    Gerard Salton and Christopher Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513–523, 1988CrossRefGoogle Scholar
  93. 93.
    Christoph Schommer. An unified definition of data mining, 2008. http://arxiv.org/PS_cache/arxiv/pdf/0809/0809.2696v1.pdf
  94. 94.
    J. Scott. Social Network Analysis. Sage, Newbury Park CA, 1992Google Scholar
  95. 95.
    Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22:888–905, 1997Google Scholar
  96. 96.
    Peilin Shi. Clustering fuzzy web transactions with rough k-means. Advanced Science and Technology, International e-Conference on, 0:48–51, 2009Google Scholar
  97. 97.
    Minseok Song and Wil M. P. van der Aalst. Towards comprehensive support for organizational mining. Decis. Support Syst., 46(1):300–317, 2008Google Scholar
  98. 98.
    J. Spacco, T. Winters, and T. Payne. Inferring use cases from unit testing. In AAAI Workshop on Educational Data Mining, pages 1–7, New York, 2006Google Scholar
  99. 99.
    Jimeng Sun, Christos Faloutsos, Spiros Papadimitriou, and Philip S. Yu. Graphscope: Parameter-free mining of large time-evolving graphs. In KDD ’07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 687–696, NY, 2007. ACMGoogle Scholar
  100. 100.
    Jimeng Sun, Yinglian Xie, Hui Zhang, and Christos Faloutsos. Less is more: Sparse graph mining with compact matrix decomposition. Stat. Anal. Data Min., 1(1):6–22, 2008Google Scholar
  101. 101.
    Hanghang Tong, Spiros Papadimitriou, Jimeng Sun, Philip S. Yu, and Christos Faloutsos. Colibri: Fast mining of large static and dynamic graphs. In KDD’08, Las Vegas, 2008Google Scholar
  102. 102.
    M. Toyoda and M. Kitsuregawa. Cerating a web community chart for navigating related communities. In HYPERTEXT ’01: Proceedings of the Twelfth ACM Conference on Hypertext and Hypermedia, pages 103–112, 2001Google Scholar
  103. 103.
    URL. http://www.google.com, 12. April 2009
  104. 104.
    URL. http://www.google.com/trends, 12. April 2009
  105. 105.
  106. 106.
  107. 107.
    W. M. P. van der Aalst, B. F. van Dongen, J. Herbst, L. Maruster, G. Schimm, and A. J. M. M. Weijters. Workflow mining: a survey of issues and approaches. Data Knowl. Eng., 47(2):237–267, 2003CrossRefGoogle Scholar
  108. 108.
    Wil M. P. Van Der Aalst, Hajo A. Reijers, and Minseok Song. Discovering social networks from event logs. Comput. Supported Coop. Work, 14(6):549–593, 2005Google Scholar
  109. 109.
    Ken Wakita and Toshiyuki Tsurumi. Finding community structure in mega-scale social networks. In Proceedings of the 18th International Conference on World Wide Web WWW 09, page 1275. ACM Press, 2007Google Scholar
  110. 110.
    Gaoxia Wang, Yi Shen, and Ming Ouyang. A vector partitioning approach to detecting community structure in complex networks. Comput. Math. Appl., 55(12):2746–2752, 2008Google Scholar
  111. 111.
    S. Wasserman and K. Faust. Social Network Analysis. Cambridge University Press, Cambridge, 1994Google Scholar
  112. 112.
    Michael V. Yudelson, Olga Medvedeva, Elizabeth Legowski, Melissa Castine, Drazen Jukic, and Rebecca S. Crowley. Mining student learning data to develop high level pedagogic strategy in a medical its. In Proceedings of AAAI Workshop on Educational Data Mining, pages 1–8, Boston, 2006Google Scholar
  113. 113.
    Jun Zhang, Mark S. Ackerman, and Lada Adamic. Expertise networks in online communities: Structure and algorithms. In WWW ’07: Proceedings of the 16th International Conference on World Wide Web, pages 221–230, NY, 2007. ACMGoogle Scholar
  114. 114.
    Y. Zhang, A. Friend, A. Traud, M. Porter, J. Fowler, and P. Mucha. Community structure in congressional cosponsorship networks. Physica A: Statistical Mechanics and its Applications, 387:1705–1712, 2008CrossRefGoogle Scholar
  115. 115.
    Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. Graph clustering based on structural/attribute similarities. Proc. VLDB Endow., 2(1):718–729, 2009Google Scholar
  116. 116.
    M.E. Zorrilla, E. Menasalvas, D. Marn, E. Mora, and J. Segovia. Web usage mining project for improving web-based learning sites. In Computer Aided Systems Theory EUROCAST 2005, volume 3643/2005 of Lecture Notes in Computer Science, chapter Web Usage Mining Project for Improving Web-Based Learning Sites. Springer, Berlin, 2005Google Scholar
  117. 117.
    Lei Zou, Lei Chen, and M. Tamer Özsu. Distance-join: pattern match query in a large graph database. Proc. VLDB Endow., 2(1):886–897, 2009Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Kateřina Slaninová
    • 1
    Email author
  • Jan Martinovič
  • Pavla Dráždilová
  • Gamila Obadi
  • Václav Snášel
  1. 1.Department of Computer Science, FEECSVŠB – Technical University of OstravaOstravaCzech Republic

Personalised recommendations