GEOSO - A Geo-Social Model: From Real-World Co-occurrences to Social Connections

As the popularity of social networks is continuously growing, collected data about online social activities is becoming an important asset enabling many applications such as target advertising, sale promotions, and marketing campaigns. Although most social interactions are recorded through online activities, we believe that social experiences taking place offline in the real physical world are equally if not more important. This paper introduces a geo-social model that derives social activities from the history of people's movements in the real world, i.e., who has been where and when. In particular, from spatiotemporal histories, we infer real-world co-occurrences - being there at the same time - and then use co-occurrences to quantify social distances between any two persons. We show that straightforward approaches either do not scale or may overestimate the strength of social connections by giving too much weight to coincidences. The experiments show that our model well captures social relationships between people, even on partially available data.

[1]  Petko Bakalov,et al.  On-line discovery of flock patterns in spatio-temporal data , 2009, GIS.

[2]  Yang Du,et al.  On Monitoring the top-k Unsafe Places , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[3]  Hai Bi,et al.  Inferring intra-organizational collaboration from cosine similarity distributions in text documents , 2009, JCDL '09.

[4]  Andrew J. Michael,et al.  Three-Dimensional Compressional Wavespeed Model, Earthquake Relocations, and Focal Mechanisms for the Parkfield, California, Region , 2006 .

[5]  I. M. Warner,et al.  Pattern Recognition of Two-Dimensional Fluorescence Data Using Cross-Correlation Analysis , 1985 .

[6]  J. Tenenbaum,et al.  Proceedings of the Annual Meeting of the Cognitive Science Society , 2015 .

[7]  Jae-Gil Lee,et al.  TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering , 2008, Proc. VLDB Endow..

[8]  Dan Cosley,et al.  Inferring social ties from geographic coincidences , 2010, Proceedings of the National Academy of Sciences.

[9]  H. Storch,et al.  Statistical Analysis in Climate Research , 2000 .

[10]  Xing Xie,et al.  Mining user similarity based on location history , 2008, GIS '08.

[11]  Jon M. Kleinberg,et al.  Wherefore art thou R3579X? , 2011, Commun. ACM.

[12]  Soe-Tsyr Yuan,et al.  Ontology-based structured cosine similarity in document summarization: with applications to mobile audio-based knowledge management , 2005, IEEE Trans. Syst. Man Cybern. Part B.

[13]  Cyrus Shahabi,et al.  Accurate Discovery of Valid Convoys from Moving Object Trajectories , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[14]  Farnoush Banaei Kashani,et al.  Efficient and Anonymous Web-Usage Mining for Web Personalization , 2003, INFORMS J. Comput..

[15]  Cynthia Dwork,et al.  Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography , 2007, WWW '07.

[16]  Seung-won Hwang,et al.  Supporting Pattern-Matching Queries over Trajectories on Road Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[17]  Frederick Mosteller,et al.  Methods for studying coincidences , 1989 .

[18]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[19]  Cyrus Shahabi,et al.  Towards integrating real-world spatiotemporal data with social networks , 2011, GIS.

[20]  Felix Waldhauser,et al.  Waveform Cross-Correlation-Based Differential Travel-Time Measurements at the Northern California Seismic Network , 2005 .

[21]  Marios Savvides,et al.  Correlation Pattern Recognition for Face Recognition , 2006, Proceedings of the IEEE.