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Modeling and Analysis of Spatiotemporal Social Networks


Complex networks, Computational social science, Data mining, Dynamic networks, Knowledge discovery, Mobile computing, Spatiotemporal analysis


Dynamic social network analysis :

Analysis of social networks focused on detecting changes in relationships between actors or broader changes in graph structure over time.

Geo-social networking :

A set of technologies which makes use of a user’s geographical position or context to provide data or enable users to publish information relevant to that context.

Expressiveness :

The capacity for the characteristics of a social networking dataset such as types of interactions within the network, and spatial and temporal granularity to capture a particular social relationship.

Community :

A structural group within a social network which embodies some shared characteristic with some measure of exclusivity.


Spatiotemporal social networks model the relationships expressed in a social network and the changes to it over time....


These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on World wide web, ACM, pp 61–70

    Google Scholar 

  • Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J (2011) Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med 8(8):e1001083. doi:10.1371/journal.pmed.1001083

    Article  Google Scholar 

  • Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content- based approach to geo-locating twitter users. In: Proceedings of the 19th ACM international conference on Information and knowledge management, ACM, pp 759–768

    Google Scholar 

  • Cho E, Myers S, Leskovec J (2011). Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '11), San Diego, USA. ACM pp 1082–1090

    Google Scholar 

  • Cohen J, Tita G (1999) Diffusion in homicide: exploring a general method for detecting spatial diffusion processes. J Quant Criminol 15(4):451–493

    Article  Google Scholar 

  • Eagle N, Pentland A, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci 106(36):15274–15278. doi:10.1073/pnas.0900282106

    Article  Google Scholar 

  • Freeman LC (2004) The development of social network analysis. Empirical Press, Vancouver

    Google Scholar 

  • George B, Shekhar S (2006) Time-aggregated graphs for modeling spatiotemporal networks. J Data Seman 11:191–212

    Google Scholar 

  • Gunturi VMV, Shekhar S, Joseph K, Carley KM (2016) Scalable computational techniques for centrality metrics on temporally detailed social network. Mach Learn:1–37. doi:10.1007/s10994-016-5583-7

    Google Scholar 

  • Habiba H, Yu Y, Berger-Wolf T, Saia J (2008) Finding spread blockers in dynamic networks. In: Proceedings of the second international conference on Advances in social network mining and analysis, Springer, vol 8, pp 55–76

    Google Scholar 

  • Lauw H, Lim E, Pang H, Tan T (2005) Social network discovery by mining spatio-temporal events. Comput Math Organ Theory 11(2):97–118. doi:10.1007/s10588-005-3939-9

    Article  MATH  Google Scholar 

  • Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031. doi:10.1002/asi.20591

    Article  Google Scholar 

  • Liben-Nowell D, Novak J, Kumar R, Raghavan P, Tomkins A (2005) Geographic routing in social networks. Proc Natl Acad Sci 102(33):11623

    Article  Google Scholar 

  • Liljeros F, Edling CR, Amaral LA, Stanley HE, Aberg Y (2001) The web of human sexual contacts. Nature 411(6840):907–908

    Article  Google Scholar 

  • Madan A, Cebrian M, Lazer D, Pentland A (2010) Social sensing for epidemiological behavior change. In: Proceedings of the 12th ACM international conference on Ubiquitous computing, ACM, pp 291–300

    Google Scholar 

  • Moon I, Carley K (2007) Modeling and simulating terrorist networks in social and geospatial dimensions. Intell Syst IEEE 22(5):40–49

    Article  Google Scholar 

  • Newman MEJ (2004) Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci U S A 101(Suppl 1):5200–5205. doi:10.1073/pnas.0307545100

    Article  Google Scholar 

  • Onnela J, Arbesman S, Gonzalez M, Barabasi AL, Christakis N (2011) Geographic constraints on social network groups. PLoS One 6(4). doi:10.1371/journal.pone.0016939

    Google Scholar 

  • Pawling A, Schoenharl T, Yan P (2008) WIPER: an emergency response system. In: Proceedings of the 5th international ISCRAM Conference, May 2008

    Google Scholar 

  • Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World wide web, ACM, pp 851–860

    Google Scholar 

  • Tang J, Musolesi M, Mascolo C, Latora V, Nicosia V (2010) Analysing information flows and key mediators through temporal centrality metrics. In: Proceedings of the 3rd workshop on social network systems, ACM, pp 1–6

    Google Scholar 

  • Tantipathananandh C, Berger-Wolf T, Kempe D (2007) A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 717–726

    Google Scholar 

  • Wang et al (2011). Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '11), San Diego, USA. ACM pp 1100–1108

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications, Structural analysis in the social sciences, vol 24. Cambridge University Press, Cambridge. doi:10.1525/ae.1997.24.1.219

    Book  MATH  Google Scholar 

  • Wei W, Carley KM (2015) Measuring temporal patterns in dynamic social networks. ACM Trans Knowl Discov Data 10:1–27

    Article  Google Scholar 

  • Wesolowski A, Eagle N (2010) Parameterizing the dynamics of slums. In: AAAI spring symposium on artificial intelligence, June 2010

    Google Scholar 

  • Yang J, Leskovec J (2010) Modeling information diffusion in implicit networks. In: Proceedings of the 2010 I.E. international conference on data mining, ICDM ‘10, pp 599–608

    Google Scholar 

  • Zhao Q, Mitra P, Chen B (2007) Temporal and information flow based event detection from social text streams. In: Proceedings of the 22nd national conference on artificial intelligence, vol 2. AAAI Press, pp 1501–1506

    Google Scholar 

  • Zheng Y, Zhou X (eds) (2011) Computing with spatial trajectories. Springer, New York

    Google Scholar 

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We are particularly grateful to the members of the Spatial Database Research Group and Dr. Karsten Steinhaeuser at the University of Minnesota for their helpful comments and valuable suggestions. We would like to thank Prof. Kathleen Carley at Carnegie Mellon University for discussion and direction. We would also like to extend our thanks to Kim Koffolt for improving the readability of this paper.

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Correspondence to Venkata M. V. Gunturi .

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Gunturi, V.M.V., Brugere, I., Shekhar, S. (2016). Modeling and Analysis of Spatiotemporal Social Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY.

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