Understanding Social Characteristic from Spatial Proximity in Mobile Social Network

Authors

  • Duan Hu Department of Electronics and Information Engineering Huazhong University of Science and Technology 1037 Luoyu Road, Wuhan, China
  • Benxiong Huang Department of Electronics and Information Engineering Huazhong University of Science and Technology 1037 Luoyu Road, Wuhan, China
  • Lai Tu Department of Electronics and Information Engineering Huazhong University of Science and Technology 1037 Luoyu Road, Wuhan, China
  • Shu Chen Department of Electronics and Information Engineering Huazhong University of Science and Technology 1037 Luoyu Road, Wuhan, China

Keywords:

Mobile social network, Geographic community, Community structure, Measurement

Abstract

Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset.

References

D. Brockmann, L. Hufnagel, and T. Geisel (2006); The scaling laws of human travel, Nature, 439:462-465. http://dx.doi.org/10.1038/nature04292

L. Hufnagel, D. Brockmann, and T. Geisel (2004); Forecast and control of epidemics in a globalized world, Proceedings of the National Academy of Sciences of the United States of America, 101(42):15124-15129.

C. Song, Z. Qu, N. Blumm, A. Barabasi (2010); Limits of predictability in human mobility, Science, 327(5968): 1018-1021. http://dx.doi.org/10.1126/science.1177170

E. Cho, S. A. Myers, and S. J. Leskovec(2011); Friendship and mobility: User movement in location-based social networks, Proc. of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, 1082-1090. http://dx.doi.org/10.1145/2020408.2020579

D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A. Barabasi (2011); Human mobility, social ties, and link prediction, Proc. of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, USA, 1100-1108.

N. Eagle, A. Pentland, and D. Lazer (2009);Inferring friendship network structure by using mobile phone data, Proc. of the National Academy of Sciences, 106(36): 15274-15278. http://dx.doi.org/10.1073/pnas.0900282106

L. Backstrom, E. Sun, and C. Marlow (2010); Find me if you can: improving geographical prediction with social and spatial proximity, Proc. of the 19th international conference on World wide web(WWW'10), New York, USA, 61-70. http://dx.doi.org/10.1145/1772690.1772698

M. C. Gonzalez, C. A.Hidalgo, and A. Barabasi (2008); Understanding individual human mobility patterns, Nature, 453: 779-782.

Understanding individual human mobility patterns, Nature, 453: 779-782. http://dx.doi.org/10.1038/nature06958

R. N. Mantegna and H. E. Stanley (1994); Stochastic process with ultraslow convergence to a Gaussian: the truncated Levy flight, Physical Review Letters, 73: 2946-2949. http://dx.doi.org/10.1103/PhysRevLett.73.2946

C. Song, T. Koren, and A. Barabasi (2010); Modelling the scaling properties of human mobility, Nature Physics, 6: 818-823. http://dx.doi.org/10.1038/nphys1760

M. T. Rivera, S. B. Soderstrom, and B. Uzzi (2010); Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms, Annual Review of Sociology, 36: 91-115. http://dx.doi.org/10.1146/annurev.soc.34.040507.134743

Q. Hao, et al. (2010); Equip tourists with knowledge mined from travelogues, Proceedings of the 19th international conference on World wide web (WWW'10), New York, USA, 401-410. http://dx.doi.org/10.1145/1772690.1772732

F. Wang, et al. (2011); Community discovery using nonnegative matrix factorization, Data Mining and Knowledge Discovery, 22: 493-521. http://dx.doi.org/10.1007/s10618-010-0181-y

Q. Li, et al. (2008); Mining user similarity based on location history, Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems(GIS'08), Irvine, CA, USA, 34-43. http://dx.doi.org/10.1145/1463434.1463477

D. Wang, T. Li, S. Zhu, and C. Ding (2008); Multi-document summarization via sentencelevel semantic analysis and symmetric matrix factorization, Proc. of the 31st annual international ACM SIGIR conference on Research and development in information retrieval( SIGIR'08 ), New York, NY, 307-314. http://dx.doi.org/10.1145/1390334.1390387

S. Fortunato (2010); Community detection in graphs, Physics Reports, 486: 75-174. http://dx.doi.org/10.1016/j.physrep.2009.11.002

L. Lu and T. Zhou (2011); Link prediction in complex networks: A survey, Physica A: Statistical Mechanics and its Applications, 390: 1150-1170.

N. P. Nguyen, et al. (2011); Adaptive algorithms for detecting community structure in dynamic social networks, Proc.IEEE INFOCOM, Shanghai,China, 2282-2290.

Published

2015-08-01

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.