[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

Community Behavior Understanding

  • Chapter
  • First Online:
Human Behavior Analysis: Sensing and Understanding

  • 716 Accesses

Abstract

The recent rapid development of smart mobile devices and mobile social networking services makes it possible to explore human behaviors in an unprecedented large scale. In this chapter, we present some of our recent research advances on community behavior understanding. Specifically, in Sect. 7.1, we present the discovering and profiling communities in mobile social networks, followed by a study on how to understand the evolution of social relationships in Sect. 7.2. Finally, in Sect. 7.3, we discuss how to enhance human social interactions by interlinking off-line and online communities.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 87.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 109.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
GBP 109.99
Price includes VAT (United Kingdom)
  • Durable hardcover 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

Institutional subscriptions

References

  1. M. E. J. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, 69, 26113–26127, 2004.

    Article  Google Scholar 

  2. S. Fortunato, Community detection in graphs, Physics Reports, 486, 3–5, pp. 75–174, 2010.

    Article  MathSciNet  Google Scholar 

  3. Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann, Link communities reveal multiscale complexity in networks, Nature, vol. 466, no. 7307, pp. 761–764, 2010.

    Article  Google Scholar 

  4. J. D. Cruz, C. Bothorel, and F. Poulet, Entropy based community detection in augmented social networks. In CASoN. IEEE, 2011, pp. 163–168.

    Google Scholar 

  5. X. Wang, L. Tang, H. Gao, and H. Liu, Discovering overlapping groups in social media, in Proc. of ICDM’10, 2010, pp. 569–578.

    Google Scholar 

  6. I. S. Dhillon, Co-clustering documents and words using bipartite spectral graph partitioning, in Proc. of KDD’01. New York, NY, USA: ACM, 2001, pp. 269–274.

    Google Scholar 

  7. M.E.J. Newman, Modularity and community structure in networks, PNAS, vol. 103, pp. 8577–8582, 2006.

    Article  Google Scholar 

  8. S. Scellato, C. Mascolo, M. Musolesi, and V. Latora, Distance matters: geo-social metrics for online social networks, in Proc. of WOSN’10. Berkeley, CA, USA: USENIX Association, 2010, pp. 8–8.

    Google Scholar 

  9. S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo, Socio-spatial properties of online location-based social networks. in Proc. of ICWSM’ 11. The AAAI Press, 2011.

    Google Scholar 

  10. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, An empirical study of geographic user activity patterns in foursquare. in Proc. of ICWSM’11. The AAAI Press, 2011, pp. 570–573.

    Google Scholar 

  11. W. Chen, H. Yin, W. Wang, L. Zhao, W. Hua, and X. Zhou, Exploiting spatio-temporal user behaviors for user linkage, in Proceedings of CIKM’17, 2017, pp. 517–526.

    Google Scholar 

  12. Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui, Exploring millions of footprints in location sharing services. in ICWSM. The AAAI Press, 2011, pp. 81–88.

    Google Scholar 

  13. J. He, X. Li, and L. Liao, Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking, in IJCAI’17, 2017. pp. 1837–1843.

    Google Scholar 

  14. M.A. Vasconcelos, S. Ricci, J. Almeida, F. Benevenuto, and V. Almeida, Tips, dones and to dos: uncovering user profiles in foursquare, in Proc. of WSDM’12. New York, NY, USA: ACM, 2012, pp. 653–662.

    Google Scholar 

  15. N. Li and G. Chen, Analysis of a location-based social network, in Proc. of CSE’09. Washington, DC, USA: IEEE Computer Society, 2009, pp. 263–270.

    Google Scholar 

  16. A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, Exploiting semantic annotations for clustering geographic areas and users in location-based social networks, in Proc. of ICWSM’11. The AAAI Press, 2011, pp. 32–35.

    Google Scholar 

  17. A. Clauset, M.E.J. Newman, and C. Moore, Finding community structure in very large networks, Physical Review E, 70, 66111–66116, 2004.

    Article  Google Scholar 

  18. K. Wakita and T. Tsurumi, Finding community structure in mega-scale social networks, in Proc. of WWW’07. New York, NY, USA: ACM, 2007, pp. 1275–1276.

    Google Scholar 

  19. S. Cavallari, V. W. Zheng, H. Cai, K. C. C. Chang, and E. Cambria, Learning community embedding with community detection and node embedding on graphs, in Proceedings of CIKM’17, 2017, pp. 377–386.

    Google Scholar 

  20. V.D. Blondel, J.L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, 2008, 10, P10008, 2008.

    Article  MATH  Google Scholar 

  21. G. Palla, I. Derenyi, I. Farkas, and T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature, vol. 435, pp. 814–818, 2005.

    Article  Google Scholar 

  22. L. Tang and H. Liu, Community detection and mining in social media, Synthesis Lectures on Data Mining and Knowledge Discovery, vol. 2, pp. 1–137, 2010.

    Article  Google Scholar 

  23. K. Steinhaeuser and N. V. Chawla, Community detection in a large real-world social network, in Social Computing, Behavioral Modeling, and Prediction, H. Liu, J. J. Salerno, and M. J. Young, Eds. Springer,New York, 2008, pp. 168–175.

    Chapter  Google Scholar 

  24. M. Hosseini-Pozveh, K. Zamanifar, and A. R. Naghsh-Nilchi, A community-based approach to identify the most influential nodes in social networks, Journal of Information Science, 43, 2, 204-220, 2017.

    Article  Google Scholar 

  25. Y. Zhou, H. Cheng, and J. X. Yu, Graph clustering based on structural/attribute similarities, Proc. VLDB Endow., vol. 2, no. 1, pp. 718–729, 2009.

    Article  Google Scholar 

  26. L. Duan, W. N. Street, Y. Liu, and H. Lu, Community detection in graphs through correlation, in KDD’14, 2014, pp. 1376–1385.

    Google Scholar 

  27. M. McPherson, L. Smith-Lovin, and J. M. Cook, Birds of a feather: Homophily in social networks, Annual Review of Sociology, vol. 27, no. 1, pp. 415–444, 2001.

    Article  Google Scholar 

  28. J. Cranshaw, E. Toch, J. Hong, A. Kittur, and N. Sadeh, Bridging the gap between physical location and online social networks, in Proc. of Ubicomp’10. New York, NY, USA: ACM, 2010, pp. 119–128.

    Google Scholar 

  29. L. Inc. Foursquare, About foursquare, April 2012. [Online]. Available: https://foursquare.com/about/

  30. [Online]. Available: https://developer.foursquare.com/docs.

  31. [Online]. Available: https://dev.twitter.com/docs.

  32. M. Ye, K. Janowicz, C. Mülligann, and W.-C. Lee, What you are is when you are: the temporal dimension of feature types in location-based social networks, in Proc. of GIS’11. New York, NY, USA: ACM, 2011, pp. 102–111.

    Google Scholar 

  33. Z. Wang, D. Zhang, X. Zhou, D. Yang, Z. Yu and Z. Yu, Discovering and profiling overlapping communities in location-based social networks, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 4, pp. 499-509, 2014. doi: https://doi.org/10.1109/TSMC.2013.2256890

    Article  Google Scholar 

  34. M. Girvan and M. E. J. Newman, Community structure in social and biological networks, PNAS, 99, 12, 7821–7826, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  35. Huynh, T., Fritz, M., Schiele, B. Discovery of activity patterns using topic models. In Ubicomp 10–19 (2008)

    Google Scholar 

  36. Liu, Y., Chen, L., Pei, J., Chen, Q., Zhao, Y.: Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In PerCom, pp. 37–46 (2007)

    Google Scholar 

  37. Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.Y.: Understanding transportation modes based on GPS data for web applications. ACM Trans. Web 4(1), 1–36 (2010)

    Article  Google Scholar 

  38. Bonneau, J., Anderson, J., Anderson, R., Stajano, F.: Eight friends are enough: social graph approximation via public listings, In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems, March 2009, 13–18 (2009)

    Google Scholar 

  39. Carley, K.M., Krackhardt, D.: Cognitive inconsistencies and non-symmetric friendship. Soc. Netw. 18(1), 1–27 (1996)

    Article  Google Scholar 

  40. Vaquera, E., Kao, G.: Do you like me as much as I like you? Friendship reciprocity and its effects on school outcomes among adolescents.Soc. Sci. Res. 37(1), 55–72 (2008)

    Article  Google Scholar 

  41. Yu, Z., Zhou, X., Becker, C., Nakamura, Y.: Tree-based mining for discovering patterns of human interaction in meetings. IEEE Trans. Knowl. Data Eng. 24(4), 759–768 (2012)

    Article  Google Scholar 

  42. Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. PNAS 106(36), 15,274–15,278 (2009)

    Article  Google Scholar 

  43. Palla, G., Barabasi, A.-L., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)

    Article  Google Scholar 

  44. Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In Proceedings of 12th International Conference on Knowledge Discovery in Data Mining (KDD 2006), 611–617.

    Google Scholar 

  45. Musiał, K., Kazienko, P.: Social networks on the Internet. World Wide Web, online, doi: https://doi.org/10.1007/s11280-011-0155-z (2012)

  46. Tang, L., Wang, X., Liu, H. Scalable learning of collective behavior. IEEE Trans. Knowl. Data Eng. online. https://doi.org/10.1109/TKDE.2011.38 (2011)

    Article  Google Scholar 

  47. Onnela, J.-P., et al.: Structure and tie strengths in mobile communication networks. PNAS 104(18), 7332–7336 (2007)

    Article  Google Scholar 

  48. Chen, J., Saad, Y.: Dense subgraph extraction with application to community detection. IEEE Trans. Knowl Data Eng, online. https://doi.org/10.1109/TKDE.2010.271 (2011)

    Article  Google Scholar 

  49. Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data 3(2), 8, 2009

    Article  Google Scholar 

  50. Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  51. Malmgren, R.D., Hofman, J.M., Amaral, L.A.N., Watts, D.J.: Characterizing individual communication patterns. In KDD, pp. 607–616 (2009)

    Google Scholar 

  52. Leskovec, J., Horvitz, E.: Planetary-scale views on a large instant-messaging network. In WWW, pp. 915–924 (2008)

    Google Scholar 

  53. Hristova, D., Musolesi, M., & Mascolo, C.: Keep Your Friends Close and Your Facebook Friends Closer: A Multiplex Network Approach to the Analysis of Offline and Online Social Ties. In ICWSM. (2014)

    Google Scholar 

  54. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453(5), 779–782 (2008)

    Article  Google Scholar 

  55. Song, C., Qu, Z., Blumm, N., Barabasi, A.-L. Limits of predictability in human mobility.Science 327 (5968), 1018–1021 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  56. Eagle, N.: Behavioral inference across cultures: using telephones as a cultural lens. IEEE Intell. Syst. 23 (4), 62–64 (2008)

    Article  Google Scholar 

  57. Wesolowski, A., Eagle, N.: Parameterizing the Dynamics of Slums. In Proceedings of the AAAI Symposium on Artificial Intelligence and Development, pp. 103–108 (2010).

    Google Scholar 

  58. Dong, Z., Song, G., Xie, K, Sun, Y., Wang, J.: Adequacy of data for mining individual friendship pattern from cellular phone call logs. The 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 573–577.

    Google Scholar 

  59. Wang, H., & Li, Z.: Region representation learning via mobility flow. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. pp. 237–246 (2017).

    Google Scholar 

  60. Zhang, J., Zheng, Y., & Qi, D.: Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI, pp. 1655–1661 (2017).

    Google Scholar 

  61. Fan, Z., Song, X., Shibasaki, R., & Adachi, R.: City Momentum: an online approach for crowd behavior prediction at a citywide level. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. pp. 559–569 (2015).

    Google Scholar 

  62. Leenders, R.T.A.J.: Evolution of friendship and best friendship choices. J. Math. Sociol. 21(1–2), 133–148 (1997)

    MATH  Google Scholar 

  63. Han, Y., & Tang, J. : Who to invite next? Predicting invitees of social groups. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. pp. 3714–3720 (2017).

    Google Scholar 

  64. Khanafiah, D., Situngkir, H.: Social balance theory: revisiting Heider’s balance theory for many agents. Technical Report, Bandung Fe Institute (2004)

    Google Scholar 

  65. Barabasi, A.-L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Phys. A 311(3–4), 590–614 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  66. Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In KDD, pp. 462–470 (2008)

    Google Scholar 

  67. Liu, Y., Goncalves, J., Ferreira, D., Hosio, S., & Kostakos, V. : Identity crisis of Ubicomp?: Mapping 15 years of the field's development and paradigm change. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. pp. 75–86 (2014)

    Google Scholar 

  68. Cui, Y., Pei, J., Tang, G., Luk, W.-S., Jiang, D., Hua, M.: Finding email correspondents in online social networks. World Wide Web, online, doi: https://doi.org/10.1007/s11280-012-0168-2 (2012)

  69. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical Report, (2005)

    Google Scholar 

  70. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)

  71. Heider, F.: The psychology of interpersonal relations. John Wiley and Sons, New York (1958)

    Book  Google Scholar 

  72. Hallinan, M.T.: The process of friendship formation. Soc. Netw.1(2), 193–210 (1978)

    Article  Google Scholar 

  73. Z. Yu, X. Zhou, D. Zhang, G. Schiele, C. Becker. Understanding social relationship evolution by using real-world sensing data. World Wide Web (2013) 16: 749. Doi: https://doi.org/10.1007/s11280-012-0189-x

    Article  Google Scholar 

  74. B. Guo, Z. Yu, D. Zhang, X. Zhou, Opportunistic IoT: Exploring the Social Side of the Internet of Things, The 16th IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD’12), Wuhan, China, 2012.

    Google Scholar 

  75. M. Conti, M. Kumar, Opportunities in opportunistic computing, Computer, 43, 1, 2010, 42–50.

    Article  Google Scholar 

  76. R. Grob, M. Kuhn, R. Wattenhofer, and M. Wirz, Cluestr: mobile social networking for enhanced group communication, Proc. of ACM GROUP, Sanibel Island, Florida, USA, 2009.

    Google Scholar 

  77. S.B. Mokhtar, et al., A Self-Organizing Directory and Matching Service for Opportunistic Social Networking, Proc. of the 3rd Workshop on Social Network Systems (SNS), Paris, France, 2010.

    Google Scholar 

  78. J. Kangasharju, J. Ott, O. Karkilahti, Floating Content: Information Availability in Urban Environments, Proc. of IEEE Percom’10, 2010.

    Google Scholar 

  79. N.D. Lane, et al., Exploiting Social Networks for Large-Scale Human Behavior Modeling, IEEE Pervasive Computing, 10, 4, 2011, 45-53.

    Article  Google Scholar 

  80. A.T. Campbell, et al., The Rise of People-Centric Sensing, IEEE Internet Computing, 12, 4, 2008, 12-21.

    Article  Google Scholar 

  81. Chiu, Chao-Min, et al. Understanding online community citizenship behaviors through social support and social identity. International Journal of Information Management 35(4) 2015: 504-519.

    Article  Google Scholar 

  82. Kim, Jooho, and Makarand Hastak. Social network analysis: Characteristics of online social networks after a disaster. International Journal of Information Management 38(1) 2018: 86-96.

    Article  Google Scholar 

  83. M. Motani, V. Srinivasan, P.S. Nuggehalli, PeopleNet: engineering a wireless virtual social network, Proc. of MobiCom’05, 2005.

    Google Scholar 

  84. D. Bottazzi et al., Context-Aware Middleware for Anytime, Anywhere Social Networks, IEEE Intelligent Systems, vol. 22, no. 5, 2007, pp. 23–32.

    Article  Google Scholar 

  85. U. Lee, J.S. Park, E. Amir, M. Gerla, ‘Fleanet: a virtual market place on vehicular networks, IEEE Transactions on Vehicular Technology, vol. 59, no. 1, 344-55, 2010.

    Article  Google Scholar 

  86. W. Hsu, T. Spyropoulos, K. Psounis, A. Helmy, Modeling Time-Variant User Mobility in Wireless Mobile Networks, Proc. of InfoCom’07, 2007, pp. 758–766.

    Google Scholar 

  87. Alim, Md Abdul, et al. Structural vulnerability assessment of community-based routing in opportunistic networks. IEEE Transactions on Mobile Computing 15(12) 2016: 3156-3170.

    Article  Google Scholar 

  88. Tao, Jun, et al. Contacts-aware opportunistic forwarding in mobile social networks: A community perspective. Wireless Communications and Networking Conference (WCNC), 2018 IEEE. IEEE, 2018.

    Google Scholar 

  89. Zhu, Konglin, et al. Data routing strategies in opportunistic mobile social networks: Taxonomy and open challenges. Computer Networks 93 (2015): 183-198.

    Article  Google Scholar 

  90. J. Tang, T. Lou, J. Kleinberg, Inferring Social Ties across Heterogeneous Networks, Proc. of WSDM’12, 2012, pp. 743–752.

    Google Scholar 

  91. J. Cranshaw, et al., Bridging the gap between physical location and online social networks, Proc. of Ubicomp ’10, Pittsburgh, PA, 2010.

    Google Scholar 

  92. D. Zhang, Z. Wang, B. Guo, V. Raychoudhury, X. Zhou, A Dynamic Community Creation Mechanism in Opportunistic Mobile Social Networks, Proc. of SocialCom 2011, MIT, USA, 2011.

    Google Scholar 

  93. T. Roughgarden, E. Tardos, How Bad is Selfish Routing? Journal of the ACM, 49, 2, 2002, 236–259.

    Article  MathSciNet  MATH  Google Scholar 

  94. Q. Li, S. Zhu and G. Cao, Routing in Selfish Delay Tolerant Networks, Proc. of InfoCom’10, 2010.

    Google Scholar 

  95. J.J. Jaramillo, R. Srikant, Darwin: Distributed and adaptive reputation mechanism for wireless ad-hoc networks, Proc. of MobiCom, 2007.

    Google Scholar 

  96. R. Ma, An incentive mechanism for P2P networks, Proc. of DCS, 2004, pp. 516–523.

    Google Scholar 

  97. M. Granovetter, The strength of weak ties, The American Journal of Sociology, vol. 78, no.6, 1973.

    Article  Google Scholar 

  98. N. Eagle, et al., Inferring Social Network Structure using Mobile Phone Data, PNAS, vol. 106, no. 36, 2007, pp. 15274-15278.

    Article  Google Scholar 

  99. B. Guo, Z. Yu, X. Zhou and D. Zhang, HybridSN: Interlinking Opportunistic and Online Communities to Augment Information Dissemination, 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, Fukuoka, 2012, pp. 188–195. doi: https://doi.org/10.1109/UIC-ATC.2012.29

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Zhiwen Yu & Zhu Wang

Authors
  1. Zhiwen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Yu, Z., Wang, Z. (2020). Community Behavior Understanding. In: Human Behavior Analysis: Sensing and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-15-2109-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2109-6_7

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2108-9

  • Online ISBN: 978-981-15-2109-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation