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Data Analysis on Location-Based Social Networks

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Mobile Social Networking

Part of the book series: Computational Social Sciences ((CSS))

Abstract

The rapid growth of location-based social networks (LBSNs) has greatly enriched people’s urban experience through social media, and attracted increasing number of users in recent years. Typical location-based social networking sites allow users to “check in” at a physical place and share the location with their online friends, and therefore bridge the gap between the real world and online social networks. The availability of large amounts of geographical and social data on LBSNs provides an unprecedented opportunity to study human mobile behavior through data analysis in a spatial–temporal–social context, enabling a variety of location-based services, from mobile marketing to disaster relief. In this chapter, we first introduce the background and framework of location-based mobile social networking. We next discuss the distinct properties, data analysis and research issues of location-based social networks, and present two illustrative examples to show the application of data mining to real-world location-based social networks.

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Notes

  1. 1.

    http://foursquare.com

  2. 2.

    http://en.wikipedia.org/wiki/Gowalla

  3. 3.

    http://www.facebook.com/about/location

  4. 4.

    http://en.wikipedia.org/wiki/Dodgeball

  5. 5.

    http://www.pcmag.com/article2/0,2817,2401433,00.asp

  6. 6.

    http://techcrunch.com/2011/02/01/google-latitude-check-in

  7. 7.

    http://www.yelp-press.com/phoenix.zhtml?c=250809&p=irol-press

  8. 8.

    http://locaccino.org

  9. 9.

    http://livehoods.org

  10. 10.

    The dataset used in this example is available at:

    http://www.public.asu.edu/~hgao16/dataset/SHTiesData.zip

  11. 11.

    The dataset used in this example is available at: http://www.public.asu.edu/~hgao16/dataset/gScorrData.zip

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Acknowledgments

  This work is supported, in part, by ONR (N000141010091). The authors would like to acknowledge all of the researchers in Arizona State University’s Data Mining and Machine Learning Laboratory. The views expressed in this chapter are solely attributed to the authors, and do not represent the opinions or policies of any of the funding agencies.

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  1. Computer Science and Engineering, Arizona State University, Phoenix, USA

    Huiji Gao & Huan Liu

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  1. Huiji Gao
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  1. Nokia, Beijing, China, People’s Republic

    Alvin Chin

  2. Institut Mines-Telecom/Telecom SudParis, Evry Cedex, France

    Daqing Zhang

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Gao, H., Liu, H. (2014). Data Analysis on Location-Based Social Networks. In: Chin, A., Zhang, D. (eds) Mobile Social Networking. Computational Social Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8579-7_8

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