Machine Learning for Auspicious Social Network Mining

Part of the Intelligent Systems Reference Library book series (ISRL, volume 65)


The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. Alongside the machine learning derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations. In this chapter, we have presented different network analysis concepts. Then we have discussed implication of machine learning for network data preparation and different learning techniques for descriptive and predictive analysis. Finally we have presented some machine learning based findings in the area of community detection, prediction, spatial-temporal and fuzzy analysis.


Social Network Mining Strategies Network data collection and Preparation Machine Learning based Network Analysis Network Learning Methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Moreno, J.L.: Emotions mapped by new geography. New York Times 3, 17 (1933)Google Scholar
  2. 2.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 452–473 (1977)Google Scholar
  3. 3.
    Biemann, C.: Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems. In: Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing, pp. 73–80. Association for Computational Linguistics (2006)Google Scholar
  4. 4.
    McGuffin, M.J.: Simple algorithms for network visualization: A tutorial. Tsinghua Science and Technology 17(4), 383–398 (2012), doi:10.1109/TST.2012.6297585Google Scholar
  5. 5.
    Crawford, C., Walshaw, C., Soper, A.: A multilevel force-directed graph drawing algorithm using multilevel global force approximation. In: 2012 16th International Conference on Information Visualisation (IV), pp. 454–459 (2012), doi:10.1109/IV.2012.78Google Scholar
  6. 6.
    Wikipedia: Graph drawing – wikipedia the free encyclopedia (2013) (Online: accessed September 11, 2013)Google Scholar
  7. 7.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: ACM SIGCOMM Computer Communication Review, vol. 29, pp. 251–262. ACM (1999)Google Scholar
  8. 8.
    Valente, T.W., Foreman, R.K.: Integration and radiality: measuring the extent of an individual’s connectedness and reachability in a network. Social Networks 20(1), 89–105 (1998)CrossRefGoogle Scholar
  9. 9.
    Kunegis, J.: On the spectral evolution of large networks. Ph.D. thesis, Koblenz, Landau (Pfalz), Univ., Diss. (2011)Google Scholar
  10. 10.
    Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the 10th ACM Conference on Electronic Commerce, EC 2009, pp. 325–334. ACM, New York (2009),, doi:10.1145/1566374.1566421Google Scholar
  11. 11.
    Yoo, A., Chow, E., Henderson, K., McLendon, W., Hendrickson, B., Catalyurek, U.: A scalable distributed parallel breadth-first search algorithm on bluegene/l. In: Proceedings of the ACM/IEEE SC 2005 Conference on Supercomputing, p. 25. IEEE (2005)Google Scholar
  12. 12.
    Korf, R.E., Schultze, P.: Large-scale parallel breadth-first search. In: AAAI, vol. 5, pp. 1380–1385 (2005)Google Scholar
  13. 13.
    Tarjan, R.: Depth-first search and linear graph algorithms. SIAM Journal on Computing 1(2), 146–160 (1972)CrossRefMATHMathSciNetGoogle Scholar
  14. 14.
    Hougardy, S.: The floyd–warshall algorithm on graphs with negative cycles. Information Processing Letters 110(8), 279–281 (2010)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Thorup, M.: Compact oracles for reachability and approximate distances in planar digraphs. Journal of the ACM (JACM) 51(6), 993–1024 (2004)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Kameda, T.: On the vector representation of the reachability in planar directed graphs. Information Processing Letters 3(3), 75–77 (1975)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Gladwell, M.: The tipping point: How little things can make a big difference. Hachette Digital, Inc. (2006)Google Scholar
  18. 18.
    Feld, S.L.: The focused organization of social ties. American Journal of Sociology, 1015–1035 (1981)Google Scholar
  19. 19.
    Dijkstra, E.: A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271 (1959),, doi:10.1007/BF01386390CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    Crauser, A., Mehlhorn, K., Meyer, U., Sanders, P.: A parallelization of dijkstra’s shortest path algorithm. In: Brim, L., Gruska, J., Zlatuška, J. (eds.) MFCS 1998. LNCS, vol. 1450, pp. 722–731. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. 21.
    Bellman, R.: On a routing problem. Quart. Appl. Math. 16, 87–90 (1958)MATHMathSciNetGoogle Scholar
  22. 22.
    Floyd, R.W.: Algorithm 97: Shortest path. Commun. ACM 5(6), 345 (1962),, doi:10.1145/367766.368168CrossRefGoogle Scholar
  23. 23.
    Johnson, D.B.: Efficient algorithms for shortest paths in sparse networks. J. ACM 24(1), 1–13 (1977),, doi:10.1145/321992.321993CrossRefMATHGoogle Scholar
  24. 24.
    Friedkin, N.E.: Structural bases of interpersonal influence in groups: A longitudinal case study. American Sociological Review, 861–872 (1993)Google Scholar
  25. 25.
    Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    Bavelas, A.: Communication patterns in task-oriented groups. The Journal of the Acoustical Society of America 22(6), 725–730 (1950)CrossRefGoogle Scholar
  27. 27.
    Beauchamp, M.A.: An improved index of centrality. Behavioral Science 10(2), 161–163 (1965)CrossRefGoogle Scholar
  28. 28.
    Moxley, R.L., Moxley, N.F.: Determining point-centrality in uncontrived social networks. Sociometry, 122–130 (1974)Google Scholar
  29. 29.
    Nieminen, U.: On the centrality in a directed graph. Social Science Research 2(4), 371–378 (1973)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Botafogo, R.A., Rivlin, E., Shneiderman, B.: Structural analysis of hypertexts: identifying hierarchies and useful metrics. ACM Transactions on Information Systems (TOIS) 10(2), 142–180 (1992)CrossRefGoogle Scholar
  31. 31.
    Hage, P., Harary, F.: Eccentricity and centrality in networks. Social Networks 17(1), 57–63 (1995)CrossRefGoogle Scholar
  32. 32.
    Bonacich, P.: Some unique properties of eigenvector centrality. Social Networks 29(4), 555–564 (2007)CrossRefGoogle Scholar
  33. 33.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefMATHGoogle Scholar
  34. 34.
    Borgatti, S.P.: Centrality and network flow. Social Networks 27(1), 55–71 (2005)CrossRefGoogle Scholar
  35. 35.
    Burt, R.S.: The social structure of competition. Networks and Organizations: Structure, Form, and Action, 57–91 (1992)Google Scholar
  36. 36.
    Zhang, E., Wang, G., Gao, K., Zhao, X., Zhang, Y.: Generalized structural holes finding algorithm by bisection in social communities. In: 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC), pp. 276–279 (2012), doi:10.1109/ICGEC.2012.98Google Scholar
  37. 37.
    Lin, C.Y., Wu, L., Wen, Z., Tong, H., Griffiths-Fisher, V., Shi, L., Lubensky, D.: Social network analysis in enterprise. Proceedings of the IEEE 100(9), 2759–2776 (2012), doi:10.1109/JPROC.2012.2203090CrossRefGoogle Scholar
  38. 38.
    Goldberg, A.V., Tarjan, R.E.: A new approach to the maximum-flow problem. Journal of the ACM (JACM) 35(4), 921–940 (1988)CrossRefMATHMathSciNetGoogle Scholar
  39. 39.
    Dantzig, G.B., Ford, L.R., Fulkerson, D.R.: A primal-dual algorithm for linear programs. Linear Inequalities and Related Systems (38), 171–181 (1956)Google Scholar
  40. 40.
    Ford, L.R., Fulkerson, D.R.: A simple algorithm for finding maximal network flows and an application to the Hitchcock problem. Rand Corporation (1955)Google Scholar
  41. 41.
    Wasserman, S.: Social network analysis: Methods and applications, vol. 8. Cambridge University Press (1994)Google Scholar
  42. 42.
    Frank, O., Strauss, D.: Markov graphs. Journal of the American Statistical Association 81(395), 832–842 (1986)CrossRefMATHMathSciNetGoogle Scholar
  43. 43.
    Wasserman, S., Pattison, P.: Logit models and logistic regressions for social networks: I. an introduction to markov graphs andp. Psychometrika 61(3), 401–425 (1996)CrossRefMATHMathSciNetGoogle Scholar
  44. 44.
    Snijders, T.A., Pattison, P.E., Robins, G.L., Handcock, M.S.: New specifications for exponential random graph models. Sociological Methodology 36(1), 99–153 (2006)CrossRefGoogle Scholar
  45. 45.
    Handcock, M.S., Robins, G., Snijders, T.A., Moody, J., Besag, J.: Assessing degeneracy in statistical models of social networks. Tech. rep., Working paper (2003)Google Scholar
  46. 46.
    Hoff, P.D., Raftery, A.E., Handcock, M.S.: Latent space approaches to social network analysis. Journal of the American Statistical Association 97(460), 1090–1098 (2002)CrossRefMATHMathSciNetGoogle Scholar
  47. 47.
    Smyth, P.: Statistical modeling of graph and network data. In: IJCAI Workshop on Learning Statistical Models from Relational Data, Citeseer (2003)Google Scholar
  48. 48.
    Tang, L., Liu, H.: Community detection and mining in social media. Synthesis Lectures on Data Mining and Knowledge Discovery 2(1), 1–137 (2010)CrossRefGoogle Scholar
  49. 49.
    Yang, Q.: Community detection and graph-based clustering. Powerpoint Presentation (2010)Google Scholar
  50. 50.
    Kubica, J., Moore, A.: Probabilistic noise identification and data cleaning. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 131–138 (2003), doi:10.1109/ICDM.2003.1250912Google Scholar
  51. 51.
    Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin 23 (2000)Google Scholar
  52. 52.
    Ghahramani, Z., Jordan, M.I.: Supervised learning from incomplete data via an em approach. In: Advances in Neural Information Processing Systems, Citeseer, vol. 6 (1994)Google Scholar
  53. 53.
    Schwarm, S., Wolfman, S.: Cleaning data with bayesian methods. Final project report for CSE574, University of Washington (2000)Google Scholar
  54. 54.
    Rundensteiner, E.: Special issue on data transformation. IEEE Techn. Bull. Data Engineering 22(1) (1999)Google Scholar
  55. 55.
    Morris, T., Verlic, M.: Clustering in depth: Methods and theory behind the clustering functionality in openrefine (2013),
  56. 56.
    Abiteboul, S., Cluet, S., Milo, T., Mogilevsky, P., Siméon, J., Zohar, S.: Tools for data translation and integration. IEEE Data Eng. Bull. 22(1), 3–8 (1999)Google Scholar
  57. 57.
    Milo, T., Zohar, S.: Using schema matching to simplify heterogeneous data translation. In: VLDB, Citeseer, vol. 98, pp. 24–27 (1998)Google Scholar
  58. 58.
    Galhardas, H., Florescu, D., Shasha, D., Simon, E.: Declaratively cleaning your data using ajax. In: Journees Bases de Donnees, Citeseer (2000)Google Scholar
  59. 59.
    Hellerstein, J.M., Stonebraker, M., Caccia, R.: Independent, open enterprise data integration. IEEE Data Eng. Bull. 22(1), 43–49 (1999)Google Scholar
  60. 60.
    Hernández, M.A., Stolfo, S.J.: Real-world data is dirty: Data cleansing and the merge/purge problem. Data Mining and Knowledge Discovery 2(1), 9–37 (1998)CrossRefGoogle Scholar
  61. 61.
    Li Lee, M., Lu, H., Ling, T.-W., Ko, Y.T.: Cleansing data for mining and warehousing. In: Bench-Capon, T.J.M., Soda, G., Tjoa, A.M. (eds.) DEXA 1999. LNCS, vol. 1677, pp. 751–760. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  62. 62.
    Monge, A.E., Elkan, C., et al.: The field matching problem: Algorithms and applications. In: KDD, pp. 267–270 (1996)Google Scholar
  63. 63.
    Cohen, W.W.: Integration of heterogeneous databases without common domains using queries based on textual similarity. In: ACM SIGMOD Record, vol. 27, pp. 201–212. ACM (1998)Google Scholar
  64. 64.
    Jolliffe, I.: Principal component analysis. Wiley Online Library (2005)Google Scholar
  65. 65.
    Galhardas, H., Florescu, D., Shasha, D., Simon, E.: Ajax: an extensible data cleaning tool. ACM SIGMOD Record 29(2), 590 (2000)CrossRefGoogle Scholar
  66. 66.
    Monge, A.E.: Matching algorithms within a duplicate detection system. IEEE Data Eng. Bull. 23(4), 14–20 (2000)Google Scholar
  67. 67.
    Jankowski, N., Grochowski, M.: Comparison of instances seletion algorithms II. Algorithms survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  68. 68.
    Jankowski, N., Grochowski, M.: Comparison of instances seletion algorithms I. Algorithms survey. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 598–603. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  69. 69.
    Knorr, E.M., Ng, R.T.: A unified notion of outliers: Properties and computation. In: KDD, pp. 219–222 (1997)Google Scholar
  70. 70.
    Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: ACM Sigmod Record, pp. 93–104. ACM (2000)Google Scholar
  71. 71.
    Hadi, A.S.: Identifying multiple outliers in multivariate data. Journal of the Royal Statistical Society. Series B (Methodological), 761–771 (1992)Google Scholar
  72. 72.
    Rocke, D.M., Woodruff, D.L.: Identification of outliers in multivariate data. Journal of the American Statistical Association 91(435), 1047–1061 (1996)CrossRefMATHMathSciNetGoogle Scholar
  73. 73.
    Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. arXiv preprint arXiv:1106.0219 (2011)Google Scholar
  74. 74.
    Liu, H., Motoda, H.: Feature selection for knowledge discovery and data mining. Springer (1998)Google Scholar
  75. 75.
    Oates, T., Jensen, D.: The e ects of training set size on decision tree complexity. In: Proceedings of the Fourteenth International Conference on Machine Learning, Citeseer (1997)Google Scholar
  76. 76.
    Reinartz, T.: A unifying view on instance selection. Data Mining and Knowledge Discovery 6(2), 191–210 (2002)CrossRefMATHMathSciNetGoogle Scholar
  77. 77.
    Lakshminarayan, K., Harp, S.A., Samad, T.: Imputation of missing data in industrial databases. Applied Intelligence 11(3), 259–275 (1999)CrossRefGoogle Scholar
  78. 78.
    Clauset, A., Moore, C., Newman, M.E.: Hierarchical structure and the prediction of missing links in networks. Nature 453(7191), 98–101 (2008)CrossRefGoogle Scholar
  79. 79.
    Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: Optics: ordering points to identify the clustering structure. ACM SIGMOD Record 28(2), 49–60 (1999)CrossRefGoogle Scholar
  80. 80.
    Ng, R.T., Han, J.: Clarans: A method for clustering objects for spatial data mining. IEEE Transactions on Knowledge and Data Engineering 14(5), 1003–1016 (2002)CrossRefGoogle Scholar
  81. 81.
    Chen, Y., Reilly, K., Sprague, A., Guan, Z.: Seqoptics: a protein sequence clustering system. BMC Bioinformatics 7(suppl. 4), S10 (2006)Google Scholar
  82. 82.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. In: ACM SIGMOD Record, vol. 25, pp. 103–114. ACM (1996)Google Scholar
  83. 83.
    Zhang, T., Ramakrishnan, R., Livny, M.: Birch: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)CrossRefGoogle Scholar
  84. 84.
    Duan, D., Li, Y., Li, R., Lu, Z.: Incremental k-clique clustering in dynamic social networks. Artificial Intelligence Review 38(2), 129–147 (2012)CrossRefGoogle Scholar
  85. 85.
    Kailing, K., Kriegel, H.P., Kröger, P.: Density-connected subspace clustering for high-dimensional data. In: Proc. SDM, vol. 4 (2004)Google Scholar
  86. 86.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B., et al.: A review of classification algorithms for eeg-based brain–computer interfaces. Journal of Neural Engineering 4 (2007)Google Scholar
  87. 87.
    Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: A review of classification techniques (2007)Google Scholar
  88. 88.
    Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. Morgan Kaufmann (2006)Google Scholar
  89. 89.
    Lao, N., Mitchell, T., Cohen, W.W.: Random walk inference and learning in a large scale knowledge base. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 529–539. Association for Computational Linguistics (2011)Google Scholar
  90. 90.
    Jaakkola, M.S.T., Szummer, M.: Partially labeled classification with markov random walks. Advances in Neural Information Processing Systems (NIPS) 14, 945–952 (2002)Google Scholar
  91. 91.
    Xu, H., Yang, Y., Wang, L., Liu, W.: Node classification in social network via a factor graph model. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 213–224. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  92. 92.
    Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 593–598. ACM (2004)Google Scholar
  93. 93.
    Benjamin, R., Parham, P.: Guilt by association: Hla-b27 and ankylosing spondylitis. Immunology Today 11, 137–142 (1990)CrossRefGoogle Scholar
  94. 94.
    Rozanov, Y.A.: Markov random fields. Springer (1982)Google Scholar
  95. 95.
    Carter, C.K., Kohn, R.: On gibbs sampling for state space models. Biometrika 81(3), 541–553 (1994)CrossRefMATHMathSciNetGoogle Scholar
  96. 96.
    Besag, J., York, J., Mollié, A.: Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics 43(1), 1–20 (1991)CrossRefMATHMathSciNetGoogle Scholar
  97. 97.
    Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Transactions on Systems, Man and Cybernetics (6), 420–433 (1976)Google Scholar
  98. 98.
    Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 467–475. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  99. 99.
    Delong, A., Boykov, Y.: A scalable graph-cut algorithm for nd grids. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  100. 100.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM (2001)Google Scholar
  101. 101.
    Johnson, C.Y.: Project ‘gaydar’: An mit experiment raises new questions about online privacy. Boston Globe (2009)Google Scholar
  102. 102.
    He, J., Chu, W.W., Liu, Z.V.: Inferring privacy information from social networks. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, F.-Y. (eds.) ISI 2006. LNCS, vol. 3975, pp. 154–165. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  103. 103.
    Lindamood, J., Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Inferring private information using social network data. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1145–1146. ACM (2009)Google Scholar
  104. 104.
    Mislove, A., Viswanath, B., Gummadi, K.P., Druschel, P.: You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 251–260. ACM (2010)Google Scholar
  105. 105.
    Mo, M., Wang, D., Li, B., Hong, D., King, I.: Exploit of online social networks with semi-supervised learning. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2010)Google Scholar
  106. 106.
    Mo, M., King, I.: Exploit of online social networks with community-based graph semi-supervised learning. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 669–678. Springer, Heidelberg (2010), CrossRefGoogle Scholar
  107. 107.
    Thomas, K., Grier, C., Nicol, D.M.: unfriendly: Multi-party privacy risks in social networks. In: Atallah, M.J., Hopper, N.J. (eds.) PETS 2010. LNCS, vol. 6205, pp. 236–252. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  108. 108.
    Zheleva, E., Getoor, L.: To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th International Conference on World Wide Web, pp. 531–540. ACM (2009)Google Scholar
  109. 109.
    Kotyuk, G., Buttyan, L.: A machine learning based approach for predicting undisclosed attributes in social networks. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 361–366 (2012), doi:10.1109/PerComW.2012.6197511Google Scholar
  110. 110.
    Fang, W., Qian, M.: Design of a platform of popular science education based on social computing. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 897–902. IEEE (2009)Google Scholar
  111. 111.
    Amershi, S., Fogarty, J., Weld, D.: Regroup: Interactive machine learning for on-demand group creation in social networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 21–30. ACM (2012)Google Scholar
  112. 112.
    Velardi, P., Navigli, R., Cucchiarelli, A., D’Antonio, F.: A new content-based model for social network analysis. In: 2008 IEEE International Conference on Semantic Computing, pp. 18–25. IEEE (2008)Google Scholar
  113. 113.
    Pinheiro, C.A.R., Helfert, M.: Mixing scores from artificial neural network and social network analysis to improve the customer loyalty. In: International Conference on Advanced Information Networking and Applications Workshops, WAINA 2009, pp. 954–959. IEEE (2009)Google Scholar
  114. 114.
    Zhou, L.: Trust based recommendation system with social network analysis. In: International Conference on Information Engineering and Computer Science, ICIECS 2009, pp. 1–4. IEEE (2009)Google Scholar
  115. 115.
    Bauer, T., Garcia, D., Colbaugh, R., Glass, K.: Detecting collaboration from behavior. In: 2013 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 13–15. IEEE (2013)Google Scholar
  116. 116.
    Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 3–55 (2001)CrossRefGoogle Scholar
  117. 117.
    Wang, C., Huberman, B.A.: How random are online social interactions? Scientific Reports 2 (2012)Google Scholar
  118. 118.
    Zhang, A.X., Noulas, A., Scellato, S., Mascolo, C.: Hoodsquare: Modeling and recommending neighborhoods in location-based social networks. arXiv preprint arXiv:1308.3657 (2013)Google Scholar
  119. 119.
    Li, N., Chen, G.: Analysis of a location-based social network. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 263–270. IEEE (2009)Google Scholar
  120. 120.
    Pelleg, D., Moore, A.W., et al.: X-means: Extending k-means with efficient estimation of the number of clusters. In: ICML, pp. 727–734 (2000)Google Scholar
  121. 121.
    Song, L., Kotz, D., Jain, R., He, X.: Evaluating location predictors with extensive wi-fi mobility data. In: Twenty-Third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, vol. 2, pp. 1414–1424. IEEE (2004)Google Scholar
  122. 122.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15,274–15,278 (2009)Google Scholar
  123. 123.
    Zheng, R., Wilkinson, D., Provost, F.: Social network collaborative filtering. Stern, IOMS Department, CeDER (2008)Google Scholar
  124. 124.
    Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)Google Scholar
  125. 125.
    Benchettara, N., Kanawati, R., Rouveirol, C.: Supervised machine learning applied to link prediction in bipartite social networks. In: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 326–330. IEEE (2010)Google Scholar
  126. 126.
    Li, X., Chen, H.: Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach. Decision Support Systems 54(2), 880–890 (2013),, doi:
  127. 127.
    Ratti, C., Sommer, C.: Approximating shortest paths in spatial social networks. In: 2012 International Conference on Privacy, Security, Risk and Trust (PASSAT), and 2012 International Confernece on Social Computing (SocialCom), pp. 585–586 (2012), doi:10.1109/SocialCom-PASSAT.2012.132Google Scholar
  128. 128.
    Stefa, J., Michalik, P.: Conversational content in the context of safety of social networks. In: 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC), pp. 137–140 (2013), doi:10.1109/ICCCyb.2013.6617576Google Scholar
  129. 129.
    Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: ACM SIGMOD Record, vol. 27, pp. 307–318. ACM (1998)Google Scholar
  130. 130.
    Domingos, P.: Mining social networks for viral marketing. IEEE Intelligent Systems 20(1), 80–82 (2005)CrossRefMathSciNetGoogle Scholar
  131. 131.
    Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th International Conference on World Wide Web, pp. 201–210. ACM (2007)Google Scholar
  132. 132.
    Brunelli, M., Fedrizzi, M.: A fuzzy approach to social network analysis. In: International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009, pp. 225–230 (2009), doi:10.1109/ASONAM.2009.72Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.S. N. Bose National Centre for Basic SciencesKolkataIndia
  2. 2.Department of Systems EngineeringAjou UniversitySuwonRepublic of Korea

Personalised recommendations