Socioscope: Human relationship and behavior analysis in social networks

IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans

Volumn 41, Issue 6, 2011, Pages 1122-1143

  Zhang, Huiqi ^a   Dantu, Ram ^a,b   Cangussu, João W ^c  

^a UNIVERSITY OF NORTH TEXAS   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c MICROSOFT   (United States)

Author keywords

Change points; reciprocity index; social groups; social networks; social relationships; socioscope

Indexed keywords

CHANGE-POINTS; RECIPROCITY INDEX; SOCIAL GROUPS; SOCIAL NETWORKS; SOCIAL RELATIONSHIPS; SOCIOSCOPE;

MINING; NETWORK SECURITY; SOCIAL NETWORKING (ONLINE); TELECOMMUNICATION EQUIPMENT;

BEHAVIORAL RESEARCH;

EID: 80054793704     PISSN: 10834427     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCA.2011.2113335     Document Type: Article

References (103)

1. S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications. Cambridge, U.K.: Cambridge Univ. Press, 1994, pp. 505-555.
2. Ogatha, "Computer supported social networking for augmenting cooperation" in Computer Supported Cooperative Work, vol. 10. Norwell, MA: Kluwer, 2001, pp. 189-209.
3. M. E. J. Newman, "Modularity and community structure in networks" Proc. Nat. Acad. Sci., vol. 103, no. 23, pp. 8577-8582, Jun. 2006.
4. A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J.Wiener, "Graph structure in the web" Comput. Netw., vol. 33, no. 2, pp. 309-320, Jun. 2000.
5. P. Doreian, V. Batageli, and A. Ferligoj, Generalized Blockmodeling, M. Granovetter, Ed. Cambridge, U.K.: Cambridge Univ. Press, 2005.
6. G. Flake, S. Lawrence, C. L. Giles, and F. Coetzee, "Self- organization and identification of web communities" IEEE Comput., vol. 35, no. 3, pp. 66-70, Mar. 2002.
7. J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Natural communities in large linked networks" in Proc. 9th Int. Conf. Knowl. Discov. Data Mining, 2003, pp. 541-546.
8. M. E. J. Newman, "Detecting community structure in networks" Eur. Phys. J., vol. B 38, no. 2, pp. 321-330, Mar. 2004.
9. C. Borgs, J. Chayes, M. Mahdian, and A. Saberi, "Exploring the community structure of newsgroups" in Proc. 10th ACM Int. Conf. Knowled. Discov. Data Mining, 2004, pp. 783-787.
10. P. Holme and M. Newman, "Nonequilibrium phase transition in the coevolution of networks and opinions" Phys. Rev. E, vol. 74, no. 5, p. 056 108, Mar. 2006.
11. P. Sarkar and A. Moore, "Dynamic social network analysis using latent space models" Proc. SIGKDD Explorations: Special Edition Link Mining, vol. 7, no. 2, pp. 31-40, Dec. 2005.
12. L. Backstrom, D. Huttenlocher, and J. Kleinberg, "Group formation in large social networks: Membership, growth, and evolution" in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 44-54.
13. Kossinets and D. Watts, "Empirical analysis of an evolving social network" Science, vol. 311, no. 5757, pp. 88-90, Jan. 2006.
14. X. Wang and A. McCallum, "Topics over time: 'A non-Markov continuous-time model of topical trends" in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 424-433.
15. R. Kumar, J. Novak, P. Raghavan, and A. Tomkins, "Structure and evolution of blogspace" Commun. ACM, vol. 47, no. 12, pp. 35-39, Dec. 2004.
16. R. Kumar, J. Novak, and A. Tomkins, "Structure and evolution of online social networks" in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 611-617.
17. A. A. Nanavati, S. Gurumurthy, G. Das, D. Chakraborty, K. Dasgupta, S. Mukherjea, and A. Joshi, "On the structural properties of massive telecom graphs: Findings and implications" in Proc. 15th ACM CIKM Conf. Inf. Knowl. Manage., 2006, pp. 435-444.
18. J. P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J. Kertesz, and A. L. Barabasi, "Structure and tie strengths in mobile communication networks" Proc. Nat. Acad. Sci. U.S.A., vol. 104, no. 18, pp. 7332-7336, May 1, 2007.
19. M. Kurucz, A. Benczur, K. Csalogany, and L. Lukacs, "Spectral clustering in telephone call graphs" in Proc. Joint 9th WEBKDD and 1st SNA-KDD Workshop, 2007, pp. 82-91.
20. W. Teng andM. Chou, "Mining communities of acquainted mobile users on call detail records" in Proc. 22nd Annu. ACM Symp. Appl. Comput., 2007, pp. 957-958.
21. G. Palla, A. Barabasi, and T. Vicsek, "Quantifying social group evolution" Nature, vol. 446, no. 7136, pp. 664-667, Apr. 2007.
22. N. Eagle, A. Pentland, and D. Lazer, "Inferring social network structure using mobile phone data" Proc. Nat. Acad. Sci. (PNAS), vol. 106, no. 36, pp. 15 274-15 278, Sep. 2009. [23] C. A. Hidalgo and C. Rodriguez-Sickert, "The dynamics of a mobile phone network" Phys. A, vol. 387, no. 12, pp. 3017-3024, May 2008.
23. C. A. Hidalgo and C. Rodriguez-Sickert, "The dynamics of a mobile phone network," Phys. A, vol. 387, no. 12, pp. 3017-3024, May 2008.
24. K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, and A. Nanavati, "Social ties and their relevance to churn in mobile telecom networks" in Proc. 11th ACM Int. Conf. Extending Database Technol.: Adv. Database Technol., 2008, pp. 668-677.
25. J. Candia, M. C. Gonzalezm, P. Wang, T. Schoenharl, G. Madey, and A. Barabasi, "Uncovering individual and collective human dynamics from mobile phone records" J. Phys. A, Math. Theoretical, vol. 41, no. 22, p. 224 015, Jun. 2008.
26. A. Pentland, "Collective intelligence" IEEE Comput. Intell. Mag., vol. 1, no. 3, pp. 9-12, Aug. 2006.
27. A. Pentland, "Automatic mapping and modeling of human networks" Phys. A, Stat. Mech. Appl., vol. 378, no. 1, pp. 59-67, May 2007.
28. N. Eagle, "Behavioral inference across cultures: Using telephones as a cultural lens" IEEE Intell. Syst., vol. 23, no. 4, pp. 62-64, Jul./Aug. 2008.
29. N. Eagle and A. Pentland, "Eigenbehaviors: Identifying structure in routine" Behav. Ecol. Sociobiol., vol. 63, no. 7, pp. 1057-1066, May 2009.
30. N. Eagle, Y. de Montjoye, and L. Bettencourt, "Community computing: Comparisons between rural and urban societies using mobile phone data" in Proc. IEEE Int. Conf. Social Comput., 2009, pp. 144-150.
31. N. Eagle, "Engineering a Common Good" in Proc. IEEE Engaging Data Conf., Boston, MA, Oct. 12-13, 2009, pp. 1-3.
32. N. Eagle, "txteagle: Mobile crowdsourcing" Int., Des. Global Develop., vol. 5623, LNCS, pp. 447-456, 2009.
33. B. Gallagher, H. Tong, T. Eliassi-Rad, and C. Faloutsos, "Using ghost edges for classification in sparsely labeled networks" in Proc. 14th ACM SIGKDD Conf., 2008, pp. 256-264.
34. P. Koutsourelakis, "Unsupervised group discovery and link prediction in relational datasets: A nonparametric Bayesian approach" Lawrence Livermore Nat. Lab. (LLNL), Livermore, CA, Tech. Rep. UCRL-TR- 230743, 2007.
35. I. Carreras, D. Miorandi, G. Canright, and K. E. Monsen, "Eigenvector centrality in highly partitioned mobile networks: Principles and applications" Adv. Biol. Inspired Inf. Syst., vol. 69, pp. 123-145, 2007.
36. L. Wang, Y. Jia, and W. Han, "Instant message clustering based on extended vector space model" Adv. Comput. Intell., vol. 4683, Springer- Verlag Lecture Notes Computer Science, pp. 435-443, 2007.
37. A. Chaintreau et al., "The diameter of opportunistic mobile networks" in Proc. ACM CoNEXT Conf., New York, 2007.
38. P. Hui, E. Yoneki, S. Y. Chan, and J. Crowcroft, "Distributed community detection in delay tolerant networks" in Proc. MobiArch, Kyoto, Japan, 2007, ACM Press.
39. N. Eagle and A. Pentland, "Reality mining: Sensing complex social systems" Pers. Ubiquitous Comput., vol. 10, no. 4, pp. 255-268, May 2006.
40. J. Lawrence, T. R. Payne, and D. De Roure, "Co-presence communities: Using pervasive computing to support weak social networks" in Proc. WETICE, 2006, pp. 149-156, IEEE Press.
41. N. Eagle, "Machine perception and learning of complex social systems" Ph.D. dissertation, Program Media Arts Sci., Massachusetts Inst. Technol., Cambridge, MA, Jun., 2005.
42. C. Kobashikawa, Y. Hatakeyama, F. Dong, and K. Hirota, "Fuzzy algorithm for group decision making with participants having finite discriminating abilities" IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 39, no. 1, pp. 86-95, Jan. 2009.
43. F. Carvalho and Y. Lechevallier, "Dynamic clustering of interval-valued data based on adaptive quadratic distances" IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 39, no. 6, pp. 1295-1306, Nov. 2009.
44. R. R. Yager, "Concept representation and database structures in fuzzy social relational networks" IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 40, no. 2, pp. 413-419, Mar. 2010.
45. U. Maulik, S. Bandyopadhyay, and I. Saha, "Integrating clustering and supervised learning for categorical data analysis" IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 40, no. 4, pp. 664-675, Jul. 2010.
46. M. Fannes and P. Spincemaile, "The mutual affinity of random measures" Periodica Math. Hungarica, vol. 47, no. 1/2, pp. 51-71, Sep. 2003.
47. A. W. Gouldner, "The norm of reciprocity: A preliminary statement" Amer. Sociol. Rev., vol. 25, no. 2, pp. 161-178, 1960.
48. L. Katz and J. Powell, "Measurement of the tendency toward reciprocation of choice" Sociometry, vol. 18, no. 4, pp. 403-409, Nov. 1955.
49. L. Katz and T. Wilson, "The variance of the number of mutual choices in sociometry" Psychometrika, vol. 21, no. 3, pp. 299-304, Sep. 1956.
50. M. Schnegg, "Reciprocity and the emergence of power laws in social networks" Int. J. Mod. Phys. C, vol. 17, no. 8, pp. 1067-1076, 2006.
51. D. Garlaschelli and M. I. Loffredo, "Patterns of link reciprocity in directed networks" Phys. Rev. Lett., vol. 93, no. 26, p. 268 701, Dec. 2004.
52. G. Zamora-López, V. Zlatić, C. Zhou, H. Ştefan?cić, and J. Kurths, "Reciprocity of networks with degree correlations and arbitrary degree sequences" Phys. Rev. E, vol. 77, no. 1, p. 016 106, Jan. 2008.
53. L. M. Floría, C. Gracia-Lázaro, J. Gómez- Gardeñes, and Y. Moreno, "Social network reciprocity as a phase transition in evolutionary cooperation" Phys. Rev. E, vol. 79, no. 2, p. 026 106, 2009.
54. B. Hogan and D. Fisher, "Email reciprocity and personal communication bias" in Proc. NetSci, Bloomington, IN, May 22-25, 2006.
55. M. Baron, "Nonparametric adaptive change-point estimation and on-line detection" Sequential Anal., vol. 19, no. 1/2, pp. 1-23, 2000.
56. M. Baron and N. Granott, "Consistent estimation of early and frequent change points" in Proc. Shoresh Conf., Y. Haitovsky, H. R. Lerche, and Y. Ritov, Eds., New York, 2003, pp. 181-192, Foundations of Statistical Inference, Springer.
57. J. W. Cangussu and M. Baron, "Automatic identification of change points for the system testing process" in Proc. 30th Annu. IEEE Int. COMPSAC, Chicago, IL, Sep. 18-21, 2006, vol. 1, pp. 377-384.
58. A. E. Raftery and V. E. Akman, "Bayesian analysis of a Poisson process with a change-point" Biometrika, vol. 73, no. 1, pp. 85-89, 1986.
59. T. Y. Yang and L. Kuo, "Bayesian binary segmentation procedure for a Poisson process with multiple change points" J. Comput. Graphical Statist., vol. 10, no. 4, pp. 772-785, Dec. 2001.
60. Y. Ritov, A. Raz, and H. Bergman, "Detection of onset of neuronal activity by allowing for heterogeneity in the change points" J. Neuroscience Methods, vol. 122, no. 1, pp. 25-42, Dec. 2002.
61. B. P. Carlin, A. E. Gelfand, and A. F. M. Smith, "Hierarchical Bayesian analysis of change point problems" Appl. Stat., vol. 41, no. 2, pp. 389- 405, 1992.
62. R. Lund and J. Reeves, "Detection of undocumented change points: A revision of the two-phase regression model" J. Clim., vol. 15, no. 17, pp. 2547-2554, Sep. 2002.
63. D. M. Hawkins, "Testing a sequence of observations for a shift in location" J. Amer. Stat. Assoc., vol. 72, no. 357, pp. 180-186, Mar. 1977.
64. K. J. Worsley, "On the likelihood ratio test for a shift in location of normal populations" J. Amer. Stat. Assoc., vol. 74, no. 366, pp. 365- 367, Jun. 1979.
65. J. Chen and A. K. Gupta, "Testing and locating change points with application to stock prices" J. Amer. Stat. Assoc., vol. 92, no. 438, pp. 739-747, Jun. 1997.
66. T. D. Johnson, R. M. Elashoff, and S. J. Harkema, "A Bayesian changepoint analysis of electromyographic data: Detecting muscle activation patterns and associated applications" Biostatistics, vol. 4, no. 1, pp. 143- 164, Jan. 2003.
67. P. Chu and X. Zhao, "Bayesian change-point analysis of tropical cyclone activity: The central North Pacific case" J. Clim., vol. 17, no. 24, pp. 4893-4901, Dec. 2004.
68. P. Fearnhead, "Exact and efficient Bayesian inference for multiple change point problems" Stat. Comput., vol. 16, no. 2, pp. 203-213, Jun. 2006.
69. K. Yamanishi and J. Takeuchi, "A unifying framework for detecting outliers and change points from time series" IEEE Trans. Knowl. Data Eng., vol. 18, no. 4, pp. 482-492, Apr. 2006.
70. C. Erdman and J.W. Emerson, "A fast Bayesian change point analysis for the segmentation of micro-array data" Bioinformatics, vol. 24, no. 19, pp. 2143-2148, Oct. 2008.
71. T. Brants and F. Chen, "A system for new event detection" in Proc. Int. ACM SIGIR Conf., 2003, pp. 330-337.
72. F. Chen, A. Farahat, and T. Brants, "Story link detection and new event detection are asymmetric" in Proc. HLT-NAACL, 2003, vol. 2, pp. 13-15.
73. G. Kumaran and J. Allan, "Text classification and named entities for new event detection" in Proc. Int. ACM SIGIR Conf., 2004, pp. 297-304.
74. X. Li and B. W. Croft, "Novelty detection based on sentence level patterns" in Proc. ACM CIKM, 2005, pp. 744-751.
75. Z. Li, B. Wang, M. Li, and W.-Y. Ma, "A probabilistic model for retrospective news event detection" in Proc. Int. SIGIR Conf., 2005, pp. 106-113.
76. Q. Zhao and P. Mitra, "Event detection and visualization for social text streams" in Proc. ICWSM, Boulder, CO, 2007.
77. Q. He, K. Chang, and E. P. Lim, "A model for anticipatory event detection" in Proc. 25th Int. Conf. Conceptual Model. (ER), vol. 4215, Springer LNCS, 2006, pp. 168-181.
78. Q. He, K. Chang, and E. P. Lim, "A model for anticipatory event detection" in Proc. 25th Int. Conf. Conceptual Model. (ER), vol. 4215, Springer LNCS, 2006, pp. 168-181. [78] Q. He, K. Chang, and E. P. Lim, "Anticipatory event detection via sentence classification" in Proc. IEEE Int. Conf. Syst., Man, Cybern., 2006, pp. 1143-1148.
79. G. Luo, C. Tang, and P. Yu, "Resource-adaptive real-time new event detection" in Proc. Int. ACM SIGMOD, 2007, pp. 497-508.
80. V. Guralnik and J. Srivastava, "Event detection from time series data" in Proc. 5th ACM SIGKDD, 1999, pp. 33-42.
81. J. Kleinberg, "Bursty and hierarchical structure in streams" in Proc. 8th ACM SIGKDD, 2002, pp. 91-101.
82. E. Keogh, S. Lonardi, and B. Chiu, "Finding surprising patterns in a time series database in linear time and space" in Proc. 8th ACM SIGKDD, 2002, pp. 550-556.
83. M. Salmenkivi and H. Mannila, "Using Markov chain Monte Carlo and dynamic programming for event sequence data" Knowl. Inf. Syst., vol. 7, no. 3, pp. 267-288, Mar. 2005.
84. A. Ihler, J. Hutchins, and P. Smyth, "Adaptive event detection with timevarying Poisson processes" in Proc. ACM SIGKDD, 2006, pp. 207-216.
85. C. C. Yang, X. Shi, and C. P. Wei, "Discovering event evolution graphs from news corpora" IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 39, no. 4, pp. 850-863, Jul. 2009.
86. J. W. Harris and H. Stocker, Handbook of Mathematics and Computational Science. New York: Springer-Verlag, 1998, p. 824.
87. S. Bregni, R. Cioffi, and M. Decina, "An empirical study on statistical properties of GSM telephone call arrivals" in Proc. IEEE Global Telecommun. Conf., 2006, pp. 1-5.
88. H. Zhang and R. Dantu, "Quantifying the presence for phone users" in Proc. 5th IEEE Consum. Commun. Netw. Conf., 2008, pp. 883-887.
89. D. Donoho and I. Johnstone, "Ideal spatial adaptation by wavelet shrinkage" Biometrica, vol. 81, no. 3, pp. 425-455, Sep. 1994.
90. D. Donoho and I. Johnstone, "Adapting to unknown smoothing via wavelet shrinkage" J. Amer. Stat. Assoc., vol. 90, no. 432, pp. 1200- 1224, Dec. 1995.
91. C. Stern, "Estimation of the mean of multivariate normal distribution" Ann. Statist., vol. 9, no. 6, pp. 1135-1151, Nov. 1981.
92. I. Johnstone and B. W. Silverman, "Wavelet thresholding estimators for data with correlated noise" J. Roy. Stat. Soc., Ser. B, vol. 59, no. 2, pp. 319-351, 1997.
93. T. Fujiki, T. Nanno, Y. Suzuki, and M. Okumura, "Identification of bursts in a document stream" in Proc. 1st Int. Workshop Knowl. Discovery Data Streams, 2004, pp. 55-64.
94. M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos, "Identifying similarities, periodicities and bursts for online search queries" in Proc. ACM SIGMOD, 2004, pp. 131-142.
95. Y. Zhu and D. Shasha, "Efficient elastic burst detection in data streams" in Proc. 9th ACM SIGKDD, 2003, pp. 336-345.
96. S. Qin, W. Qian, and A. Zhou, "Adaptively detecting aggregation bursts in data streams" in Proc. 10th Int. Conf. Database Syst. Adv. Appl., vol. 3453, LNCS, 2005, pp. 435-446.
97. T. Chen, Y. Wang, B. Fang, and J. Zheng, "Detecting lasting and abrupt bursts in data streams using two-layered wavelet tree" in Proc. Adv. Int. Conf. Telecommun., 2006, p. 30.
98. M. Wang, T. M. Madhyastha, N. H. Chan, S. Papadimitriou, and C. Faloutsos, "Data mining meets performance evaluation: Fast algorithms for modeling bursty traffic" in Proc. 18th Int. Conf. Data Eng., 2002, pp. 507-516.
99. G. P. C. Fung, J. X. Yu, P. S. Yu, and H. Lu, "Parameter free bursty events detection in text streams" in Proc. 31st Int. Conf. Very Large Data Bases, 2005, pp. 181-192.
100. Z. Yuan, Y. Jia, and S. Yang, "Online burst detection over high speed short text streams" in Proc. Int. Conf. Comput. Sci., 2007, pp. 717-725.
101. M. Vlachos, K. Wu, S. Chen, and P. Yu, "Fast burst correlation of financial data" in Proc. 9th Eur. Conf. Princ. Pract. Knowl. Discovery Databases, 2005, pp. 368-379.
102. N. Nilsson, Artificial Intelligence, A New Synthesis, 1st ed. San Fransisco, CA: Morgan Kaufmann, 1998, pp. 317-336.
103. Massachusetts Institute of Technology, Reality Mining, 2009. [Online]. Available: