A Framework for Analysing Dynamic Communities in Large-scale Social Networks

Vítor Cerqueira, Márcia Oliveira, João Gama

2015

Abstract

Telecommunications companies must process large-scale social networks that reveal the communication patterns among their customers. These networks are dynamic in nature as new customers appear, old customers leave, and the interaction among customers changes over time. One way to uncover the evolution patterns of such entities is by monitoring the evolution of the communities they belong to. Large-scale networks typically comprise thousands, or hundreds of thousands, of communities and not all of them are worth monitoring, or interesting from the business perspective. Several methods have been proposed for tracking the evolution of groups of entities in dynamic networks but these methods lack strategies to effectively extract knowledge and insight from the analysis. In this paper we tackle this problem by proposing an integrated business-oriented framework to track and interpret the evolution of communities in very large networks. The framework encompasses several steps such as network sampling, community detection, community selection, monitoring of dynamic communities and rule-based interpretation of community evolutionary profiles. The usefulness of the proposed framework is illustrated using a real-world large-scale social network from a major telecommunications company.

References

  1. Asur, S., Parthasarathy, S., and Ucar, D. (2009). An eventbased framework for characterizing the evolutionary behavior of interaction graphs. ACM Transactions on Knowledge Discovery from Data, 3(4):16:1-16:36.
  2. Berger-Wolf, T. Y. and Saia, J. (2006). A framework for analysis of dynamic social networks. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 7806, pages 523-528, New York, NY, USA. ACM.
  3. Birant, D. (2011). Knowledge-oriented applications in data mining. In (Ed.), P. K. F., editor, Data Mining Using RFM Analysis, pages 91-108. INTECH Open Access Publisher.
  4. Blondel, V., Guillaume, J., Lambiotte, R., and Mech, E. (2008). Fast unfolding of communities in large networks. J. Stat. Mech, 10:1-12.
  5. Brodka, P., Saganowski, S., and Kazienko, P. (2013). Ged: the method for group evolution discovery in social networks. Social Network Analysis and Mining, 3(1):1-14.
  6. Clauset, A., Newman, M. E. J., and Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6):066111.
  7. Domingos, P. and Richardson, M. (2001). Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 57-66. ACM.
  8. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5):75 - 174.
  9. Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821-7826.
  10. Gjoka, M., Kurant, M., Butts, C. T., and Markopoulou, A. (2010). Walking in Facebook: A Case Study of Unbiased Sampling of OSNs. In Proceedings of IEEE INFOCOM 7810, INFOCOM'10, pages 2498-2506, San Diego, California, USA. IEEE Press.
  11. Miglautsch, J. R. (2000). Thoughts on RFM scoring. The Journal of Database Marketing, 8(1):67-72.
  12. Nanavati, A. A., Singh, R., Chakraborty, D., Dasgupta, K., Mukherjea, S., Das, G., Gurumurthy, S., and Joshi, A. (2008). Analyzing the structure and evolution of massive telecom graphs. Knowledge and Data Engineering, IEEE Transactions on, 20(5):703-718.
  13. Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in networks. Phys. Rev. E, 69(2):026113.
  14. Oliveira, M. and Gama, J. (2012). An overview of social network analysis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(2):99-115.
  15. Oliveira, M. D. B. and Gama, J. a. (2010). Mec - monitoring clusters' transitions. In Agotnes, T., editor, STAIRS, volume 222 of Frontiers in Artificial Intelligence and Applications, pages 212-224. IOS Press.
  16. Oliveira, M. D. B., Guerreiro, A., and Gama, J. (2014). Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows. Social Netw. Analys. Mining, 4(1):1-19.
  17. Palla, G., Derényi, I., Farkas, I., and Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043):814-818.
  18. Quinlan, J. R. (1993). C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  19. Team, R. D. C. (2008). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0.
  20. Verbeke, W., Martens, D., and Baesens, B. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14:431-446.
  21. Wang, Y., Cong, G., Song, G., and Xie, K. (2010). Community-based greedy algorithm for mining topk influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1039-1048. ACM.
Download


Paper Citation


in Harvard Style

Cerqueira V., Oliveira M. and Gama J. (2015). A Framework for Analysing Dynamic Communities in Large-scale Social Networks . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-096-3, pages 235-242. DOI: 10.5220/0005345602350242


in Bibtex Style

@conference{iceis15,
author={Vítor Cerqueira and Márcia Oliveira and João Gama},
title={A Framework for Analysing Dynamic Communities in Large-scale Social Networks},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2015},
pages={235-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005345602350242},
isbn={978-989-758-096-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Framework for Analysing Dynamic Communities in Large-scale Social Networks
SN - 978-989-758-096-3
AU - Cerqueira V.
AU - Oliveira M.
AU - Gama J.
PY - 2015
SP - 235
EP - 242
DO - 10.5220/0005345602350242