Clustering in Geo-Social Networks

The rapid growth of Geo-Social Networks (GeoSNs) provides a new and rich form of data. Users of GeoSNs can capture their geographic locations and share them with other users via an operation named checkin. Thus, GeoSNs can track the connections (and the time of these connections) of geographic data to their users. In addition, the users are organized in a social network, which can be extended to a heterogeneous network if the connections to places via checkins are also considered. The goal of this paper is to analyze the opportunities in clustering this rich form of data. We first present a model for clustering geographic locations, based on GeoSN data. Then, we discuss how this model can be extended to consider temporal information from checkins. Finally, we study how the accuracy of community detection approaches can be improved by taking into account the checkins of users in a GeoSN.

[1]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.

[2]  Arthur C. Sanderson,et al.  Pattern Trajectory Analysis of Nonstationary Multivariate Data , 1980, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Paulo Shakarian,et al.  Mining for geographically disperse communities in social networks by leveraging distance modularity , 2013, KDD.

[4]  Ruifang Liu,et al.  Weighted Graph Clustering for Community Detection of Large Social Networks , 2014, ITQM.

[5]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

[6]  Ioannis Stavrakakis,et al.  ISCoDe: A framework for interest similarity-based community detection in social networks , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[7]  David Sankoff,et al.  Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison , 1983 .

[8]  Nikos Mamoulis,et al.  Density-based place clustering in geo-social networks , 2014, SIGMOD Conference.

[9]  Chih-Ya Shen,et al.  On socio-spatial group query for location-based social networks , 2012, KDD.

[10]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[11]  Marco A. Casanova,et al.  ComeTogether: Discovering Communities of Places in Mobility Data , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[12]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[13]  Jennifer Widom,et al.  SimRank: a measure of structural-context similarity , 2002, KDD.

[14]  Scott Daly,et al.  Digital Images and Human Vision , 1993 .

[15]  M. Karplus,et al.  Collective motions in proteins: A covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations , 1991, Proteins.

[16]  Jure Leskovec,et al.  Community Detection in Networks with Node Attributes , 2013, 2013 IEEE 13th International Conference on Data Mining.