On maximum clique problems in very large graphs
External memory algorithms
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Spreading Activation Models for Trust Propagation
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
On the structural properties of massive telecom call graphs: findings and implications
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Employing Latent Dirichlet Allocation for fraud detection in telecommunications
Pattern Recognition Letters
Mining call and mobility data to improve paging efficiency in cellular networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mobile call graphs: beyond power-law and lognormal distributions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Comparing clustering schemes at two levels of granularity for mobile call mining
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Content consumption cartography of the paris urban region using cellular probe data
Proceedings of the first workshop on Urban networking
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Telecommunication applications based on user modeling focus on extracting customer behavior and preferences from the information implicitly included in Call Detail Record (CDR) datasets Even though there are many different application areas (fraud detection, viral and targeted marketing, churn prediction, etc.) they all share a common data source (CDRs) and a common set of features for modeling the user In this paper we present our experience with different applications areas in generating user models from massive real datasets of both mobile phone and landline subscriber activity We present the analysis of a dataset containing the traces of 50,000 mobile phone users and 50,000 landline users from the same geographical area for a period of six months and compare the different behaviors when using landlines and mobile phones and the implications that such differences have for each application Our results indicate that user models for a variety of applications can be generated efficiently and in a homogeneous way using an architecture based on distributed computing and that there are numerous differences between mobile phone and landline users that have relevant practical implications.