ABSTRACT
Communities in real life are usually dynamic and community structures evolve over time. Detecting community evolution provides insight into the underlying behavior of the network. A growing body of study is devoted in studying the dynamics of communities in evolving social networks. Most of them provide an event-based framework to characterize and track the community evolution. A part of these studies take a step further and provide a predictive model of the events by exploiting community features. However, the proposed models require the community extraction and computing the community features relevant to the time point to be predicted. In this paper, we proposed a new approach for predicting events by estimating feature values related to the communities in a given network. An event-based framework is used to characterize community behavior patterns. Then, a time series ARIMA model is used to predict how particular community features will change in the following time period. Distinct time windows are examined in constituting and analyzing time series. Our proposed approach efficiently tracks similar communities and identifies events over time. Furthermore, community feature values are forecasted with an acceptable error rate. Event prediction using forecasted feature values substantially match up with actual events.
- M. Girvan and M. E. J. Newman, "Community structure in social and biological networks," Proceedings of the National Academy of Sciences, vol. 99, no. 12, pp. 7821--7826, 2002.Google ScholarCross Ref
- A. Clauset, M. E. J. Newman, and C. Moore, "Finding community structure in very large networks," Physical Review E, pp. 1-- 6, 2004. {Online}. Available: www.ece.unm.edu/ifis/papers/communitymoore.pdfGoogle Scholar
- G. Palla, I. Derényi, I. Farkas, and T. Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society," Nature, vol. 435, no. 7043, pp. 814--818, June 2005. {Online}. Available: http://dx.doi.org/10.1038/nature03607Google ScholarCross Ref
- J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Tracking evolving communities in large linked networks," Proceedings of the National Academy of Sciences of the United States of America, vol. 101, no. Suppl 1, pp. 5249--5253, Apr. 2004. {Online}. Available: http://www.pnas.org/content/101/suppl.1/5249.abstractGoogle ScholarCross Ref
- C. Tantipathananandh, T. Berger-Wolf, and D. Kempe, "A framework for community identification in dynamic social networks," in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '07. New York, NY, USA: ACM, 2007, pp. 717--726. {Online}. Available: http://doi.acm.org/10.1145/1281192.1281269 Google ScholarDigital Library
- Z. Chen, W. Hendrix, and N. F. Samatova, "Community-based anomaly detection in evolutionary networks," J. Intell. Inf. Syst., vol. 39, no. 1, pp. 59--85, Aug. 2012. {Online}. Available: http://dx.doi.org/10.1007/s10844-011-0183-2 Google ScholarDigital Library
- B. Gliwa, P. Bródka, A. Zygmunt, S. Saganowski, P. Kazienko, and J. Kozlak, "Different approaches to community evolution prediction in blogosphere." CoRR, vol. abs/1306.3517, 2013. Google ScholarDigital Library
- N. İlhan and Şule Gündüz Öğüdücü, "Community event prediction in dynamic social networks," Machine Learning and Applications, vol. 2, pp. 269--274, 2013.Google Scholar
- S. Asur, S. Parthasarathy, and D. Ucar, "An event-based framework for characterizing the evolutionary behavior of interaction graphs." in KDD, P. Berkhin, R. Caruana, and X. Wu, Eds. ACM, 2007, pp. 913--921. Google ScholarDigital Library
- L. Backstrom, D. Huttenlocher, J. Kleinberg, and X. Lan, "Group formation in large social networks: Membership, growth, and evolution," in Proceedings of KDD'06, 2006. Google ScholarDigital Library
- R. Kumar, J. Novak, and A. Tomkins, "Structure and evolution of online social networks," in KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. New York, NY, USA: ACM, 2006, pp. 611--617. Google ScholarDigital Library
- G. Palla, A.-L. Barabasi, and T. Vicsek, "Quantifying social group evolution," Nature, vol. 446, no. 7136, pp. 664--667, April 2007.Google ScholarCross Ref
- M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zaïane, "Community evolution mining in dynamic social networks," Procedia - Social and Behavioral Sciences, vol. 22, no. 0, pp. 49 -- 58, 2011.Google ScholarCross Ref
- M. Takaffoli, F. Sangi, J. Fagnan, and O. R. Zaïane, "A framework for analyzing dynamic social networks," 2010.Google Scholar
- P. Bródka, P. Kazienko, and B. Koloszczyk, "Predicting group evolution in the social network." in SocInfo, ser. Lecture Notes in Computer Science, vol. 7710. Springer, 2012, pp. 54--67. Google ScholarDigital Library
- S. Huang and D. Lee, "Exploring structural features in predicting social network evolution," Machine Learning and Applications, vol. 2, pp. 269--274, 2011. Google ScholarDigital Library
- Z. Huang and D. K. J. Lin, "The time-series link prediction problem with applications in communication surveillance," INFORMS Journal on Computing, pp. 286--303, 2009. Google ScholarDigital Library
- P. R. da Silva Soares and R. B. C. Prudêncio, "Time series based link prediction." in IJCNN. IEEE, 2012, pp. 1--7.Google Scholar
- R. Michalski, P. Kazienko, and D. Krol, "Predicting social network measures using machine learning approach." in ASONAM. IEEE Computer Society, 2012, pp. 1056--1059. Google ScholarDigital Library
- D. Greene, D. Doyle, and P. Cunningham, "Tracking the evolution of communities in dynamic social networks," in Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining, ser. ASONAM '10. Washington, DC, USA: IEEE Computer Society, 2010, pp. 176--183. Google ScholarDigital Library
- V. Kawadia and S. Sreenivasan, "Online detection of temporal communities in evolving networks by estrangement confinement," CoRR, vol. abs/1203.5126, 2012.Google Scholar
- V. Blondel, J. Guillaume, R. Lambiotte, and E. Mech, "Fast unfolding of communities in large networks," J. Stat. Mech, p. P10008, 2008.Google ScholarCross Ref
- J. Hopcroft, O. Khan, B. Kulis, and B. Selman, "Tracking evolving communities in large linked networks," Proceedings of the National Academy of Sciences, vol. 101, pp. 5249--5253, April 2004.Google ScholarCross Ref
- G. E. Box and G. M. Jenkins, "Some recent advances in forecasting and control," Applied Statistics, pp. 91--109, 1968.Google ScholarCross Ref
- R. J. Hyndman and Y. Kh, "Automatic time series forecasting: The forecast package for r," Journal of Statistical Software, 2008.Google ScholarCross Ref
- R Development Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2008, ISBN 3-900051-07-0. {Online}. Available: http://www.R-project.orgGoogle Scholar
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The weka data mining software: An update," SIGKDD Explor. Newsl., vol. 11, no. 1, pp. 10--18, Nov. 2009. {Online}. Available: http://doi.acm.org/10.1145/1656274.1656278 Google ScholarDigital Library
- Predicting Community Evolution based on Time Series Modeling
Recommendations
Community evolution prediction in dynamic social networks using community features' change rates
SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied ComputingIn this paper, we investigate the prediction of community future occurring events in dynamic social networks, based on change rates of features that describe a community throughout its evolution life-cycle rather than absolute values of features. ...
Community Acknowledgment: Engaging Community Members in Volunteer Acknowledgment
GROUPVolunteers in non-profit groups are a valuable workforce that contributes to economic development and supports people in need in the U.S. However, many non-profit groups face challenges including engaging and sustaining volunteer participation, as well ...
Community-based service ecosystem evolution analysis
AbstractServices are flourishing dramatically, and continuously increasing interactions among them are resulting in a new phenomenon called “service ecosystems,” which has become a focus of academia and industry. Driven by technology innovation, changes ...
Comments