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Evolution in Social Networks: A Survey

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Social Network Data Analytics

Abstract

There is much research on social network analysis but only recently did scholars turn their attention to the volatility of social networks. An abundance of questions emerged. How does a social network evolve – can we find laws and derive models that explain its evolution? How do communities emerge in a social network and how do they expand or shrink? What is a community in an evolving network – can we claim that two communities seen at two distinct timepoints are the same one, even if they have next to no members in common? Research advances have different perspectives: some scholars focus on how evolution manifests itself in a social network, while others investigate how individual communities evolve as new members join and old ones become inactive. There are methods for discovering communities and capturing their changes in time, and methods that consider a community as a smoothly evolving constellation and thus build and adapt models upon that premise. This survey organizes advances on evolution in social networks into a common framework and gives an overview of these different perspectives.

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References

  1. C. Aggarwal, J. Han, J. Wang, and P. Yu. A framework for clustering evolving data streams. In 29th Int. Conf. on Very Large Data Bases (VLDB’03), Berlin, Germany, 2003.

    Google Scholar 

  2. Charu Aggarwal and Philip Yu. Online analysis of community evolution in data streams. In Proc. of SIAM Int. Conf. on Data Mining (SDM’05), 2005.

    Google Scholar 

  3. Amr Ahmed and Eric Xing. Recovering time-varying networks of dependencies in social and biological studies. PNAS, 106:11878–1188, July 2009.

    Article  Google Scholar 

  4. L. AlSumait, Daniel Barbara, and Carlotta Domeniconi. On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking. In Proc. of 2008 IEEE Conf. on Data Mining (ICDM’08), pages 373–382, Pisa, Italy, Dec. 2008. IEEE.

    Google Scholar 

  5. Sitaram Asur, Srinivasan Parthasarathy, and Duygu Ucar. An eventbased framework for characterizing the evolutionary behavior of interaction graphs. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD’07), pages 913–921, New York, NY, USA, 2007. ACM.

    Google Scholar 

  6. Sitaram Asur, Srinivasan Parthasarathy, and Duygu Ucar. An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans. Knowl. Discov. Data, 3(4):1–36, 2009.

    Article  Google Scholar 

  7. Albert Bifet and Ricard Gavaldá. Learning from time-changing data with adaptive windowing. In SIAM Int. Conf. on Data Mining (SDM’07), 2007.

    Google Scholar 

  8. David M. Blei and John D. Lafferty. Dynamic topic models. In Proc. of 23rd Int. Conf. on Machine Learning (ICML’06), Pittsburgh, PA, 2006.

    Google Scholar 

  9. Deepayan Chakrabarti, Christos Faloutsos, and Mary McGlohon. Graph mining: Laws and generators. In Charu Aggarwal and Haixun Wang, editors, Managing and Mining Graph Data, pages 67–121. 2010.

    Google Scholar 

  10. Deepayan Chakrabarti, Ravi Kumar, and Andrew Tomkins. Evolutionary clustering. In Proceedings of 12th ACM SIGKDD Int. Conf (KDD’06), pages 554–560, Philadelphia, PA, Aug. 2006. ACM.

    Google Scholar 

  11. Yun Chi, Xiaodan Song, Dengyong Zhou, Koji Hino, and Belle Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. In Proceedings of 13th ACMSIGKDD Int. Conf (KDD’07), San Jose, CA, Aug. 2007. ACM.

    Google Scholar 

  12. Martin Ester, Hans-Peter Kriegel, Jörg Sander, MichaelWimmer, and Xiaowei Xu. Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases, pages 323–333, New York City, New York, USA, August 1998. Morgan Kaufmann.

    Google Scholar 

  13. Tanja Falkowski, Jörg Bartelheimer, and Myra Spiliopoulou. Community dynamics mining. In Proc. of 14th European Conf. on Information Systems (ECIS’2006), Göteborg, SWEDEN, June 2006.

    Google Scholar 

  14. Tanja Falkowski, Jörg Bartelheimer, and Myra Spiliopoulou. Mining and visualizing the evolution of subgroups in social networks. InWI ’06: Pro ceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 52–58, Washington, DC, USA, 2006. IEEE Computer Society.

    Google Scholar 

  15. Tanja Falkowski, Anja Barth, and Myra Spiliopoulou. Studying community dynamics with an incremental graph mining algorithm. In Proc. of the 14th Americas Conference on Information Systems, Toronto, Canada, Aug. 2008.

    Google Scholar 

  16. Tanja Falkowski and Myra Spiliopoulou. Users in volatile communities: Studying active participation and community evolution. In C. Conati, K.McCoy, and G. Paliouras, editors, Proceedings of UserModeling 2007 - LNAI 4511, pages 57–66. Springer, 2007.

    Google Scholar 

  17. Jure Ferlez, Christos Faloutsos, Jure Leskovec, Dunja Mladenic, and Marko Grobelnik. Monitoring network evolution using MDL. In Proceedings of IEEE Int. Conf. on Data Engineering (ICDE’08). IEEE, 2008.

    Google Scholar 

  18. Santo Fortunato. Community detection in graphs. Physics Report, 486:75–174, Feb. 2010.

    Article  MathSciNet  Google Scholar 

  19. David Gibson, Ravi Kumar, and Andrew Tomkins. Discovering large dense subgraphs in massive graphs. In 31st Int. Conf. on Very Large Data Bases (VLDBâ˘A´Z05), pages 721–732, Trondheim, Norway, 2005.

    Google Scholar 

  20. 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 

  21. Andre Gohr, Alexander Hinneburg, Rene Schult, and Myra Spiliopoulou. Topic evolution in a stream of documents. In SIAM Data Mining Conf. (SDM’09), pages 378–385, Reno, CA, Apr.-May 2009.

    Google Scholar 

  22. S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O’Callaghan. Clustering data streams: Theory and practice. IEEE Trans. of Knowledge and Data Eng., 15(3):515–528, 2003.

    Article  Google Scholar 

  23. Ata Kabán and Mark Girolami. A dynamic probabilistic model to visualise topic evolution in text streams. Journal of Intelligent Information Systems, 18(2/3):107–125, 2002.

    Article  Google Scholar 

  24. Yehuda Koren. Collaborative filtering with temporal dynamics. Communications of ACM, 53(4):89–97, 2010.

    Article  Google Scholar 

  25. Ravi Kumar, Jasmine Novak, and Andrew Tomkins. Structure and evolution of online social networks. In Proceedings of 12th ACM SIGKDD Int. Conf (KDD’06), Philadelphia, PA, Aug. 2006. ACM.

    Google Scholar 

  26. Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins. Microscopic evolution of social networks. In Proceedings of 14th ACM SIGKDD Int. Conf (KDD’08), Las Vegas, Nevada, Aug. 2007. ACM.

    Google Scholar 

  27. Jure Leskovec and Christos Faloutsos. Sampling from large graphs. In Proceedings of 12th ACM SIGKDD Int. Conf (KDD’06), Philadelphia, PA, Aug. 2006. ACM.

    Google Scholar 

  28. Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle Tseng. FacetNet: A framework for analyzing communities and their evolutions in dynamic networks. In Proceedings of World Wide Web Int. Conf. (WWW’08), pages 685–694, Beijing, China, Apr. 2008. ACM.

    Google Scholar 

  29. Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle Tseng. Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data, 3(2), 2009.

    Google Scholar 

  30. Yu-Ru Lin, Jimeng Sun, Nanl Cao, and Shixia Liu. ContexTour: Contextual contour visual analysis on dynamic multi-relational clustering. In Proceedings of SIAM Data Mining Conf. (SDM’10), pages 418–429. SIAM, Apr. 2010.

    Google Scholar 

  31. Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi Konuru, Hari Sundaram, and Aisling Kelliher. MetaFac: Community discovery via relational hypergraph factorization. In Proceedings of 16th ACM SIGKDD Int. Conf. (KDD’09), Paris, France, June-July 2009. ACM.

    Google Scholar 

  32. Qiaozhu Mei and ChengXiang Zhai. Discovering Evolutionary Theme Patterns from Text - An Exploration of Temporal Text Mining. In Proc. of 11th ACMSIGKDD Int. Conf. on Knowledge Discovery and DataMining (KDD’05), pages 198–207, Chicago, IL, Aug. 2005. ACM Press.

    Google Scholar 

  33. Satoshi Moringa and Kenji Yamanichi. Tracking Dynamics of Topic Trends Using a Finite Mixture Model. In Proc.of 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’04), pages 811–816, Seattle, Washington, Aug. 2004. ACM Press.

    Google Scholar 

  34. Olfa Nasraoui, Cesar Cardona-Uribe, and CArsols Rojas-Coronel. Tecno-Streams: Tracking evolving clusters in noisy data streams with an scalable immune system learning method. In Proc. IEEE Int. Conf. on Data Mining (ICDM’03), Melbourne, Australia, 2003.

    Google Scholar 

  35. M.E.J. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical Review, E69(026113), 2004.

    Google Scholar 

  36. Gergely Palla, Albert-László Barabási, and Tamás Vicske. Quantifying social group evolution. Nature, 446(7136):664–667, Apr. 2007.

    Article  Google Scholar 

  37. Purnamrita Sarkar and Andrew W. Moore. Dynamic social network analysis using latent space models. SIGKDD Explorations Newsletter, 7(2):31–40, 2005.

    Article  Google Scholar 

  38. Zaigham Faraz Siddiqui and Myra Spiliopoulou. Combining multiple interrelated streams for incremental clustering. In Proceedings of 21st International Conference on Scientific and Statistical Database Management, SSDBM ’09, pages 535–552, New Orleans, May 2009.

    Google Scholar 

  39. Zaigham Faraz Siddiqui and Myra Spiliopoulou. Stream clustering of growing objects. In Proceedings of Discovery Science ’09, Oporto, Portugal, Oct. 2009.

    Google Scholar 

  40. Zaigham Faraz Siddiqui and Myra Spiliopoulou. Tree induction over a stream of perennial objects (to be published in july 2010). In Proc of 22nd International Conference on Scientific and Statistical Database Management, SSDBM ’10. Springer-Verlag, 2010.

    Google Scholar 

  41. Myra Spiliopoulou, Irene Ntoutsi, Yannis Theodoridis, and Rene Schult. Monic – modeling and monitoring cluster transitions. In Proc. of 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’06), pages 706–711, Philadelphia, USA, Aug. 2006. ACM.

    Google Scholar 

  42. Jimeng Sun, Christos Faloutsos, S. Papadimitriou, and Philip Yu. Graph-Scope: Parameter-free mining of large time-evolving graphs. In Proceedings of 13th ACM SIGKDD Int. Conf (KDD’07), pages 687–696, San Jose, CA, Aug. 2007. ACM.

    Google Scholar 

  43. Jimeng Sun, D. Tao, and Christos Faloutsos. Beyond streams and graphs: Dynamic tensor analysis. In Proceedings of 12th ACMSIGKDD Int. Conf (KDD’06), Philadelphia, PA, Aug. 2006. ACM.

    Google Scholar 

  44. Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Transactions on Knowledge and Data Engineering, 22(2):179–192, Feb. 2010.

    Article  Google Scholar 

  45. Chayant Tantipathananandh, Tanya Berger-Wolf, and David Kempe. A framework for community identification in dynamic social networks. In KDD ’07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 717–726, New York, NY, USA, 2007. ACM.

    Google Scholar 

  46. Masashi Toyoda and Masaru Kitsuregawa. Extracting evolution of web communities from a series of web archives. In Proc. of the 14th ACM Conference on Hypertext and Hypermedia (HYPERTEXT ’03), pages 28–37, Nottingham, UK, 2003. ACM.

    Google Scholar 

  47. Xuerui Wang and Andrew McCallum. Topics over time: a non-Markov continuous-time model of topical trends. In Proceedings of 12th ACM SIGKDD Int. Conf (KDD’06), pages 424–433, Philadelphia, PA, Aug. 2006. ACM.

    Google Scholar 

  48. Tianbao Yang, Yun Chi, Shenguo Zhu, Yihong Gong, and Rong Jin. A bayesian approach toward finding communities and their evolutions in dynamic social networks. In SIAM Int. Conf. on Data Mining (SDM’09), pages 990–1001, Reno, CA, Apr.-May 2009.

    Google Scholar 

  49. Jian Zhang. A survey on streaming algorithms for massive graphs. In Charu Aggarwal and Haixun Wang, editors, Managing and Mining Graph Data, pages 391–418. 2010.

    Google Scholar 

  50. Shuheng Zhou, John Lafferty, and Larry Wasserman. Time varying undirected graphs. Machine Learning, 80:295–319, 2010. DOI: 10.1007/s10994-010-5180-0.

    Article  Google Scholar 

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Authors and Affiliations

  1. Otto-von-Guericke-University Magdeburg, Magdeburg, Germany

    Myra Spiliopoulou

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  1. Myra Spiliopoulou
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Correspondence to Myra Spiliopoulou .

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  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, New York, USA

    Charu C. Aggarwal

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Spiliopoulou, M. (2011). Evolution in Social Networks: A Survey. In: Aggarwal, C. (eds) Social Network Data Analytics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8462-3_6

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  • DOI: https://doi.org/10.1007/978-1-4419-8462-3_6

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