Emergence of scaling in human-interest dynamics

Scientific Reports

Volumn 3, Issue , 2013, Pages

  Zhao, Zhi Dan ^a,b   Yang, Zimo ^a   Zhang, Zike ^a,c   Zhou, Tao ^a   Huang, Zi Gang ^b,d   Lai, Ying Cheng ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b Arizona State University   (United States)

^c HANGZHOU NORMAL UNIVERSITY   (China)

^d LANZHOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; INFORMATION PROCESSING; PSYCHOLOGICAL MODEL;

DATASETS AS TOPIC; HUMANS; MODELS, PSYCHOLOGICAL;

EID: 84890675107     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep03472     Document Type: Article

References (63)

1. Barabási, A.-L. The architecture of complexity. IEEE Contr. Syst. Mag. 27, 33-42 (2007). (Pubitemid 47232164)
2. Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591-646 (2009).
3. Guimerà, R., Llorente, A., Moro, E. & Sales-Pardo, M. Predicting human preferences using the block structure of complex social networks. PLoS ONE 7, e44620 (2012).
4. Lü, L. et al. Recommender systems. Phys. Rep. 519, 1-49 (2012).
5. Lazer, D. et al. Life in the network: the coming age of computational social science. Science 323, 721 (2009).
6. Wu, F. & Huberman, B. A. Novelty and collective attention. Proc. Natl. Acad. Sci. U. S. A. 104, 17599-17601 (2007). (Pubitemid 350210881)
7. Weng, L., Flammini, A., Vespignani, A. & Menczer, F. Competition among memes in a world with limited attention. Sci. Rep. 2, 335 (2012).
8. Ye, M., Sandholm, T., Wang, C., Aperjis, C. & Huberman, B. A. Collective attention and the dynamics of group deals. In WWW '12 Companion: Proc. 21st Int. Conf.WWW, Lyon, France. New York, NY, USA: ACM. (2012, April 16-20).
9. Brin, S. & Page, L. The anatomy of a large-scale hypertextual web search engine. Comp. Net. ISDN 30, 107-117 (1998).
10. Craswell, N. & Szummer, M. Random walks on the click graph. In SIGIR '07: Proc. 30th Annual Int. ACM SIGIR CRDIR, Amsterdam, The Netherlands. New York, NY, USA: ACM. (2007, July 23-27).
11. Fagin, R. et al. Random walks with "back buttons". Ann. Appl. Probab. 810-862 (2001).
12. Meiss, M. R., Gonçalves, B., Ramasco, J. J., Flammini, A. & Menczer, F. Agents, bookmarks and clicks: a topical model of web navigation. In HT '10: Proc. 21st ACM CHH, Toronto, Ontario, Canada. New York, NY, USA: ACM. (2010, June 13-16).
13. Chierichetti, F., Kumar, R., Raghavan, P. & Sarlós, T. Are web users really markovian? In WWW '12: Proc. 21st Int. Conf.WWW, Lyon, France. New York, NY, USA: ACM. (2012, April 16-20).
14. Barabási, A.-L. The origin of bursts and heavy tails in human dynamics. Nature 435, 207-211 (2005). (Pubitemid 40685946)
15. Oliveira, J. G. & Barabási, A.-L. Human dynamics: Darwin and einstein correspondence patterns. Nature 437, 1251-1251 (2005).
16. Dezsö, Z. et al. Dynamics of information access on the web. Phys. Rev. E 73, 066132 (2006).
17. Zhou, T., Kiet, H. A. T., Kim, B. J., Wang, B. H. & Holme, P. Role of activity in human dynamics. Europhys. Lett. 82, 28002 (2008).
18. Gonçalves, B. & Ramasco, J. J. Human dynamics revealed through web analytics. Phys. Rev. E 78 (2008).
19. Wu, Y., Zhou, C., Xiao, J., Kurths, J. & Schellnhuber, H. J. Evidence for a bimodal distribution in human communication. Proc. Natl. Acad. Sci. U. S. A. 107, 18803-18808 (2010).
20. Vázquez, A. et al.Modeling bursts and heavy tails in human dynamics. Phys. Rev. E 73, 036127 (2006).
21. Malmgren, R. D., Stouffer, D. B., Motter, A. E. & Amaral, L. A. N. A poissonian explanation for heavy tails in e-mail communication. Proc. Natl. Acad. Sci. U. S. A. 105, 18153-18158 (2008).
22. Malmgren, R. D., Stouffer, D. B., Campanharo, A. S. L. O. & Amaral, L. A. N. On universality in human correspondence activity. Science 325, 1696-1700 (2009).
23. Han, X.-P., Zhou, T. & Wang, B.-H. Modeling human dynamics with adaptive interest. New J. Phys. 10, 073010 (2008).
24. Vázquez, A. Impact of memory on human dynamics. Physica A 373, 747-752 (2007). (Pubitemid 44647672)
25. Oliveira, J. G. & Vázquez, A. Impact of interactions on human dynamics. Physica A 388, 187-192 (2009).
26. Min, B., Goh, K. I. & Kim, I. M. Waiting time dynamics of priority-queue networks. Phys. Rev. E 79 (2009).
27. Brockmann, D.,Hufnagel, L.&Geisel, T. The scaling laws of human travel. Nature 439, 462-465 (2006). (Pubitemid 43152835)
28. Gonzalez, M. C., Hidalgo, C. A. & Barabási, A.-L. Understanding individual human mobility patterns. Nature 453, 779-782 (2008). (Pubitemid 351793783)
29. Rhee, I. et al. On the levy-walk nature of human mobility. IEEE/ACM Trans. Net. 19, 630-643 (2011).
30. Song, C., Koren, T., Wang, P.& Barabási, A.-L.Modelling the scaling properties of human mobility. Nat. Phys. 6, 818-823 (2010).
31. Han, X.-P., Hao, Q.,Wang, B.-H. & Zhou, T. Origin of the scaling law in human mobility: Hierarchy of traffic systems. Phys. Rev. E 83, 036117 (2011).
32. Yan, X.-Y., Han, X.-P., Zhou, T. & Wang, B.-H. Exact solution of the gyration radius of an individual's trajectory for a simplified human regular mobility model. Chin. Phys. Lett. 28, 120506 (2011).
33. Huberman, B. A., Pirolli, P. L. T., Pitkow, J. E. & Lukose, R. M. Strong regularities in world wide web surfing. Science 280, 95-97 (1998). (Pubitemid 28169088)
34. Stehlé, J., Barrat, A. & Bianconi, G. Dynamical and bursty interactions in social networks. Phys. Rev. E 81, 035101 (2010).
35. Karsai, M., Kaski, K., Barabási, A.-L. & Kertész, J. Universal features of correlated bursty behaviour. Sci. Rep. 2, 397 (2012).
36. Lam, W. & Mostafa, J. Modeling user interest shift using a bayesian approach. J. Am. Soc. Inf. Sci. Tech. 52, 416-429 (2001). (Pubitemid 32248665)
37. White, R. W., Bailey, P. & Chen, L. Predicting user interests from contextual information. In SIGIR '09: Proc. 32nd Int. ACM SIGIR CRDIR, Boston, MA, USA. New York, NY, USA: ACM. (2009, July 19-23).
38. Chmiel, A., Kowalska, K. & Hoyst, J. Scaling of human behavior during portal browsing. Phys. Rev. E 80, 066122 (2009).
39. Yang, S. et al. Like like alike: joint friendship and interest propagation in social networks. In WWW '11: Proc. 20th Int. Conf. WWW, Hyderabad, India. New York, NY, USA: ACM. (2011, March 28-April 1).
40. Kingman, J. F. C. The exponential decay of markov transition probabilities. Proc. London Math. Soc. 3, 337-358 (1963).
41. Yamasaki, K., Muchnik, L., Havlin, S., Bunde, A. & Stanley, H. E. Scaling and memory in volatility return intervals in financial markets. Proc. Natl. Acad. Sci. U. S. A. 102, 9424 (2005).
42. Goh, K. I. & Barabási, A.-L. Burstiness and memory in complex systems. Europhys. Lett. 81, 48002 (2008).
43. Cai, S. M., Fu, Z. Q., Zhou, T., Gu, J. & Zhou, P. L. Scaling and memory in recurrence intervals of internet traffic. Europhys. Lett. 87, 68001 (2009).
44. Szell, M., Sinatra, R., Petri, G., Thurner, S. & Latora, V. Understanding mobility in a social petri dish. Sci. Rep. 2, 457 (2012).
45. Busemeyer, J. R.&Townsend, J. T. Decision field theory. Psychol. Rev. 100, 432-59 (1993).
46. Salganik, M. J., Dodds, P. & Watts, D. J. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854-856 (2006). (Pubitemid 43228850)
47. Banavar, J. R.,Maritan, A. & Rinaldo, A. Size and form in efficient transportation networks. Nature 399, 130-132 (1999).
48. Rosvall, M.&Bergstrom, C. T.Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. U. S. A. 105, 1118-1123 (2008). (Pubitemid 351397014)
49. Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327, 1018-1021 (2010).
50. Bagrow, J. P. & Lin, Y.-R. Mesoscopic structure and social aspects of human mobility. PLoS ONE 7, e37676 (2012).
51. Barabási, A.-L. & Albert, R. Emergence of scaling in random networks. Science 286, 509-512 (1999).
52. Gonçalves, B., Meiss, M. R., Ramasco, J. J., Flammini, A. & Menczer, F. Remembering what we like: Toward an agent-based model of web traffic. In WSDM '09: The 2nd ACM Int. Conf. WSDM, Barcelona, Spain. New York, NY, USA: ACM. (2009, February 9-12).
53. Sorribes, A., Armendariz, B. G., Lopez-Pigozzi, D., Murga, C. & de Polavieja, G. G. The origin of behavioral bursts in decision-making circuitry. PLoS Comp. Bio. 7, e1002075 (2011).
54. Antal, T. & Redner, S. The excited random walk in one dimension. J. Phys. A 38, 2555 (2005).
55. Vespignani, A. Predicting the behavior of techno-social systems. Science 325, 425-428 (2009).
56. Balcan, D. & Vespignani, A. Phase transitions in contagion processes mediated by recurrent mobility patterns. Nat. Phys. 7, 581-586 (2011).
57. Zhao, Z.-D., Liu, Y. & Tang, M. Epidemic variability in hierarchical geographical networks with human activity patterns. Chaos 22, 023150 (2012).
58. Spiliopoulou, M.,Mobasher, B., Berendt, B. & Nakagawa, M. A framework for the evaluation of session reconstruction heuristics in web-usage analysis. INFORMS J. Comp. 15, 171-190 (2003). (Pubitemid 39042727)
59. Borges, J. & Levene, M. Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Trans. Knowl. Data En. 19, 441-452 (2007).
60. Meiss, M., Duncan, J., Gonçalves, B., Ramasco, J. J. & Menczer, F. What's in a session: tracking individual behavior on the web. In HT '09: Proc. 21st ACM CHH, Torino, Italy. New York, NY, USA: ACM. (2009, June 13-16).
61. Fortunato, S., Flammini, A., Menczer, F. & Vespignani, A. Topical interests and the mitigation of search engine bias. Proc. Natl. Acad. Sci. U. S. A. 103, 12684-12689 (2006). (Pubitemid 44298621)
62. Zhou, T., Zhao, Z.-D., Yang, Z. & Zhou, C. Relative clock verifies endogenous bursts of human dynamics. Europhys. Lett. 97, 18006 (2012).
63. Clauset, A., Shalizi, C. & Newman, M. E. J. Power-law distributions in empirical data. SIAM Rev. 51, 661-703 (2009).