Abstract
How do social groups, such as Facebook groups and Wechat groups, dynamically evolve over time? How do people join the social groups, uniformly or with burst? What is the pattern of people quitting from groups? Is there a simple universal model to depict the come-and-go patterns of various groups?
In this article, we examine temporal evolution patterns of more than 100 thousands social groups with more than 10 million users. We surprisingly find that the evolution patterns of real social groups goes far beyond the classic dynamic models like SI and SIR. For example, we observe both diffusion and non-diffusion mechanism in the group joining process, and power-law decay in group quitting process, rather than exponential decay as expected in SIR model. Therefore, we propose a new model comeNgo, a concise yet flexible dynamic model for group evolution. Our model has the following advantages: (a) Unification power: it generalizes earlier theoretical models and different joining and quitting mechanisms we find from observation. (b) Succinctness and interpretability: it contains only six parameters with clear physical meanings. (c) Accuracy: it can capture various kinds of group evolution patterns preciously, and the goodness of fit increases by 58% over baseline. (d) Usefulness: it can be used in multiple application scenarios, such as forecasting and pattern discovery. Furthermore, our model can provide insights about different evolution patterns of social groups, and we also find that group structure and its evolution has notable relations with temporal patterns of group evolution.
- Roy M. Anderson, Robert M. May, and B. Anderson. 1992. Infectious Diseases of Humans: Dynamics and Control. Vol. 28. Wiley Online Library.Google Scholar
- Lars Backstrom, Dan Huttenlocher, Jon Kleinberg, and Xiangyang Lan. 2006. Group formation in large social networks: Membership, growth, and evolution. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 44--54. Google ScholarDigital Library
- Ronald S. Burt. 1993. The social structure of competition. Explorations in Economic Sociology 65 (1993), 103.Google Scholar
- Damon Centola. 2010. The spread of behavior in an online social network experiment. Science 329, 5996 (2010), 1194--1197. Google ScholarCross Ref
- James S. Coleman and James Samuel Coleman. 1994. Foundations of Social Theory. Harvard University Press.Google Scholar
- Riley Crane and Didier Sornette. 2008. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences 105, 41 (2008), 15649--15653. Google ScholarCross Ref
- Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, and Christopher Potts. 2013. No country for old members: User lifecycle and linguistic change in online communities. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 307--318. Google ScholarDigital Library
- Nicolas Ducheneaut, Nicholas Yee, Eric Nickell, and Robert J. Moore. 2007. The life and death of online gaming communities: A look at guilds in world of warcraft. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 839--848. Google ScholarDigital Library
- Robin I. M. Dunbar. 1993. Coevolution of neocortical size, group size and language in humans. Behavioral and Brain Sciences 16, 4 (1993), 681--694. Google ScholarCross Ref
- Mark S. Granovetter. 1973. The strength of weak ties. American Journal of Sociology (1973), 1360--1380.<?qru msg="AU: Please provide the volume number for Ref. [8]."?>Google Scholar
- Sanjay Ram Kairam, Dan J. Wang, and Jure Leskovec. 2012. The life and death of online groups: Predicting group growth and longevity. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining. ACM, 673--682. Google ScholarDigital Library
- William O. Kermack and Anderson G. McKendrick. 1927. A contribution to the mathematical theory of epidemics. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 115. The Royal Society, 700--721. Google ScholarCross Ref
- Ravi Kumar, Jasmine Novak, Prabhakar Raghavan, and Andrew Tomkins. 2005. On the bursty evolution of blogspace. World Wide Web 8, 2 (2005), 159--178. Google ScholarDigital Library
- Theodoros Lappas, Evimaria Terzi, Dimitrios Gunopulos, and Heikki Mannila. 2010. Finding effectors in social networks. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1059--1068. Google ScholarDigital Library
- Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins. 2008. Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 462--470. Google ScholarDigital Library
- Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005. Graphs over time: Densification laws, shrinking diameters and possible explanations. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 177--187. Google ScholarDigital Library
- Jure Leskovec, Mary McGlohon, Christos Faloutsos, Natalie S. Glance, and Matthew Hurst. 2007. Patterns of cascading behavior in large blog graphs. SDM 7 (2007), 551--556. Google ScholarCross Ref
- Kenneth Levenberg. 1944. A method for the solution of certain non--linear problems in least squares. Quarterly of Applied Mathematics 2, 2 (1944), 164--168. Google ScholarCross Ref
- Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L. Tseng. 2009. Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD) 3, 2 (2009), 8.Google Scholar
- Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, and Christos Faloutsos. 2012. Rise and fall patterns of information diffusion: Model and implications. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 6--14. Google ScholarDigital Library
- Stanley Milgram. 1967. The small world problem. Psychology today 2, 1 (1967), 60--67.Google Scholar
- Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. ACM, 29--42. Google ScholarDigital Library
- Joao Gama Oliveira and Albert-László Barabási. 2005. Human dynamics: Darwin and einstein correspondence patterns. Nature 437, 7063 (2005), 1251--1251. Google ScholarCross Ref
- J.-P. Onnela, Jari Saramäki, Jorkki Hyvönen, György Szabó, David Lazer, Kimmo Kaski, János Kertész, and A.-L. Barabási. 2007. Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences 104, 18 (2007), 7332--7336. Google ScholarCross Ref
- Gergely Palla, Albert-László Barabási, and Tamás Vicsek. 2007. Quantifying social group evolution. Nature 446, 7136 (2007), 664--667. Google ScholarCross Ref
- Alexei Vázquez, João Gama Oliveira, Zoltán Dezsö, Kwang-Il Goh, Imre Kondor, and Albert-László Barabási. 2006. Modeling bursts and heavy tails in human dynamics. Physical Review E 73, 3 (2006), 036127.Google ScholarCross Ref
- Yang Wang, Deepayan Chakrabarti, Chenxi Wang, and Christos Faloutsos. 2003. Epidemic spreading in real networks: An eigenvalue viewpoint. In Proceedings of the 22nd International Symposium on Reliable Distributed Systems. IEEE, 25--34. Google ScholarCross Ref
- Linyun Yu, Peng Cui, Fei Wang, Chaoming Song, and Shiqiang Yang. 2015. From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics. In Proceedings of the International Conference on Data Mining. IEEE, 559--568. DOI:http://dx.doi.org/10.1109/ICDM.2015.79. Google ScholarDigital Library
Index Terms
- comeNgo: A Dynamic Model for Social Group Evolution
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