Abstract
Graphs are a very important abstraction to model complex structures and respective interactions, with a broad range of applications including web analysis, telecommunications, chemical informatics and bioinformatics. In this work we are interested in the application of graph mining to identify abnormal behavior patterns from telecom Call Detail Records (CDRs). Such behaviors could also be used to model essential business tasks in telecom, for example churning, fraud, or marketing strategies, where the number of customers is typically quite large. Therefore, it is important to rank the most interesting patterns for further analysis. We propose a vertex relevant ranking score as a unified measure for focusing the search of abnormal patterns in weighted call graphs based on CDRs. Classical graph-vertex measures usually expose a quantitative perspective of vertices in telecom call graphs. We aggregate wellknown vertex measures for handling attribute-based information usually provided by CDRs. Experimental evaluation carried out with real data streams, from a local mobile telecom company, showed us the feasibility of the proposed strategy.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Alves, R., Ferreira, P.G., Belo, O., Lopes, J., Ribeiro, J., Cortesao, L., Martins, F.: Discovering telecom fraud situations through mining anomalous behavior patterns. In: Proceedings of the DMBA Workshop, on the 12th ACM SIGKDD (2006)
Chakrabarti, D., Faloutsos, C.: Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1), article 2 (June 2006)
Cortes, C., Pregibon, D., Volinsky, C.: Communities of interest. Intelligent Data Analysis 6, 211–219 (2002)
Cortes, C., Pregibon, D., Volinsky, C.: Computational methods for dynamic graphs. Journal of Computational and Graphical Statistics 12, 950–970 (2003)
Euler, T.: Churn Prediction in Telecommunications Using Mining Mart. In: Proceedings of the DMBiz Workshop, on the 9th European Conference on Principles and Practice in Knowledge Discovery in Databases, PKDD 2005 (2005)
Ferreira, P., Alves, R., Belo, O., Cortesão, L.: Establishing Fraud Detection Patterns Based on Signatures. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 526–538. Springer, Heidelberg (2006)
Nanavati, A.A., Gurumurthy, S., Das, G., Chakraborty, D., Dasgupta, K., Mukherjea, S., Joshi, A.: On the structural properties of massive telecom call graphs: findings and implications. In: Proceedings of CIKM 2006, pp. 435–444 (2006)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. (1999)
Pennock, D.M., Flake, G.W., Lawrence, S., Glover, E.J., Giles, C.L.: Winners don’t take all: Characterizing the competition for links on the web. In: Proceedings of Proc. Natl. Acad. Sci. USA, pp. 5207–5211 (2002)
Akoglu, L., McGlohon, M., Faloutsos, C.: oddball: Spotting Anomalies in Weighted Graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6119, pp. 410–421. Springer, Heidelberg (2010)
Aggarwal, C.C., Zhao, Y., Yu, P.S.: Outlier detection in graph streams. In: Proceedings of ICDE 2011, pp. 399–409 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alves, R., Ferreira, P., Ribeiro, J., Belo, O. (2012). Detecting Abnormal Patterns in Call Graphs Based on the Aggregation of Relevant Vertex Measures. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-31488-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31487-2
Online ISBN: 978-3-642-31488-9
eBook Packages: Computer ScienceComputer Science (R0)