LBSNSim: Analyzing and modeling location-based social networks

The soaring adoption of location-based social networks (LBSNs) makes it possible to analyze human socio-spatial behaviors based on large-scale realistic data, which is important to both the research community and the design of new location-based social applications. However, performing direct measurements on LBSNs is impractical, because of the security mechanisms of existing LBSNs, and high time and resource costs. The problem is exacerbated by the scarcity of available LBSN datasets, which is mainly due to the privacy concerns and the hardness of distributing large-volume data. As a result, only a very few number of LBSN datasets are publicly released. In this paper, we extract and study the universal statistical features of three LBSN datasets, and propose LBSNSim, a trace-driven model for generating synthetic LBSN datasets capturing the properties of the original datasets. Our evaluation shows that LBSNSim provides an accurate representation of target LBSNs.

[1]  Michael R. Lyu,et al.  Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks , 2012, AAAI.

[2]  John Krumm,et al.  Inference Attacks on Location Tracks , 2007, Pervasive.

[3]  Vitaly Shmatikov,et al.  De-anonymizing Social Networks , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[4]  Jure Leskovec,et al.  Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model , 2011, UAI.

[5]  G. Caldarelli,et al.  Preferential attachment in the growth of social networks, the Internet encyclopedia wikipedia , 2007 .

[6]  John Zimmerman,et al.  I'm the mayor of my house: examining why people use foursquare - a social-driven location sharing application , 2011, CHI.

[7]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[8]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[9]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[10]  Cecilia Mascolo,et al.  A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[11]  Fengyuan Xu,et al.  SybilDefender: Defend against sybil attacks in large social networks , 2012, 2012 Proceedings IEEE INFOCOM.

[12]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[13]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[14]  Cecilia Mascolo,et al.  Socio-Spatial Properties of Online Location-Based Social Networks , 2011, ICWSM.

[15]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[16]  Ben Y. Zhao,et al.  Measurement-calibrated graph models for social network experiments , 2010, WWW '10.

[17]  Jennifer Neville,et al.  Fast Generation of Large Scale Social Networks While Incorporating Transitive Closures , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[18]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[19]  Ben Y. Zhao,et al.  Sharing graphs using differentially private graph models , 2011, IMC '11.