Skip to main content

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

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7108))

Abstract

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.

This paper is a full version of a poster paper appeared in ACMGIS’2011 [19].

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Crandall, D., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. National Academy of Sciences 107(52), 22436–22441 (2010)

    Article  Google Scholar 

  2. Shahabi, C., Banaei-Kashani, F.: Efficient and anonymous web usage mining for web personalization. INFORMS Journal on Computing-Special Issue on Data Mining 15(2) (2003)

    Google Scholar 

  3. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining User Similarity Based on Location History. In: Proc. Of the 16th ACM SIGSPATIAL International Conference on Advances of GIS, New York, NY (2008)

    Google Scholar 

  4. Storch, H., Zwiers, F.: Statistical Analysis in Climate Research. Cambridge University Pr. (2001); ISBN 0521012309

    Google Scholar 

  5. Diaconis, P., Mosteller, F.: Methods for Studying Coincidences. J. Am. Stat. Assoc. 84, 853–861 (1989)

    Article  MathSciNet  Google Scholar 

  6. Backstrom, L., Dwok, C., Kleinberg, J.: Wherefore Art Throu R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. In: Proc. of the 16th International World Wide Web Conference (2007)

    Google Scholar 

  7. Schaff, D.P., Waldhauser, F.: Waveform cross-correlation-based differential travel-time measurements at the northern nalifornianeismic network. Bull. Seism. Soc. Am. 96, 38–49 (2006)

    Article  Google Scholar 

  8. Rossi, T.M., Warner, I.M.: Pattern Recognition of Two-Dimensional Fluorescence Data Using Cross-Correlation Analysis. Applied Spectroscopy 39(6), 949–959 (1985)

    Article  Google Scholar 

  9. Yuan, S.T., Sun, J.: Ontology-based structured cosine similarity in document summarization: with applications to mobile audio-based knowledge management. IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics 35(5) (2005)

    Google Scholar 

  10. Esteva, M., Bi, H.: Inferring Intra-organizational Collaboration from Best-matched Cosine Similarity Distributions in Text. In: Proc. of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL (2009)

    Google Scholar 

  11. Zhang, D., Du, Y., Hu, L.: On Monitoring the top-k Unsafe Places. In: Proc. of 24th International Conference on Data Engineering (ICDE), Cancun, Mexico (2008)

    Google Scholar 

  12. Flicker, http://www.flickr.com/

  13. LiveJournal, http://www.livejournal.com

  14. IMDB, http://www.imdb.com

  15. Kumar, B.V., Savvides, M., Xie, C.: Correlation pattern regconition for face recognition. Proc. of the IEEE (2006)

    Google Scholar 

  16. Yuan, S.T., Sun, J.: Ontology-based Structured Cosine Similarity in Document Summarization: with Applications to Mobile Audio-Based Knowledge Management. IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics 35(5) (2005)

    Google Scholar 

  17. Griffiths, T., Tenenbaum, J.: Randomness and Coincidences: Reconciling Intuition and Probability Theory. In: Proc. of the 23rd Annual Conference of the Cognitive Science Society, pp. 370–375 (2001)

    Google Scholar 

  18. Wikipedia, http://www.wikipedia.org

  19. Pham, H., Hu, L., Shahabi, C.: Towards Integrating Real-World Spatiotemporal Data with Social Networks. In: Proc. of the 19th ACM SIGSPATIAL International Conference on Advances in GIS, Poster Presentation, Chicago, Illinois (2011)

    Google Scholar 

  20. Yoon, H., Shahabi, C.: Accurate Discovery of Valid Convoys from Moving Object Trajectories. In: International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM 2009), Miami, Florida, USA (2009)

    Google Scholar 

  21. Lee, J.G., Han, J., Whang, K.Y.: Trajectory Clustering: a Partition-and-Group Framework. In: SIGMOD Conference, pp. 593–604 (2007)

    Google Scholar 

  22. Lee, J.G., Han, J., Li, X., Gonzalez, H.: TraClass: Trajectory Classification using Hierarchical Region-Based and Trajectory-Based Clustering. Proc. of the VLDB Endowment  1(1) (2008)

    Google Scholar 

  23. Vieira, M.R., Bakalov, P., Tsotras, V.J.: On-line Discovery of Flock Patterns in Spatio-Temporal Data. In: GIS, pp. 286–295 (2009)

    Google Scholar 

  24. Roh, G.P., Roh, J.W., Hwang, S.W., Yi, B.K.: Supporting Pattern Matching Queries over Trajectories on Road Networks. TKDE (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pham, H., Hu, L., Shahabi, C. (2011). GEOSO - A Geo-Social Model: From Real-World Co-occurrences to Social Connections. In: Kikuchi, S., Madaan, A., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2011. Lecture Notes in Computer Science, vol 7108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25731-5_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25731-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25730-8

  • Online ISBN: 978-3-642-25731-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics