NDLI: Churn detection in large user networks

Content Provider	IEEE Xplore Digital Library
Author	Deri, J.A. Moura, J.M.F.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA (Deri, J.A.; Moura, J.M.F.)
Abstract	Anomaly detection on dynamic real-world networks such as large caller networks and online social networks is a very difficult problem, analogous to looking for a needle in a haystack. This paper considers detecting churners in a 3.7 million mobile phone network. The two main issues are designing fast and efficient features and classifiers. We discuss both in this paper. We associate every caller in the network with an activity vector and an affinity graph, and our features are derived from activity levels computed from subgraphs of the affinity graph. These features reflect the graph-dependent nature of the problem. To compute these networks expeditiously, we extend as integral affinity graphs the concept of integral images. Our anomaly classifier is a cascaded classifier with stages that combine naive Bayes and decision tree classifiers. Simulations with a 3.7 million cell phone user network illustrate an anomaly classifier that reaches a false alarm rate of 0.8% with a churn detection rate of 71%.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	1090
Ending Page	1094
File Size	120989
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6853765
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Decision trees Feature extraction Conferences Signal processing Computational modeling Cellular phones cascaded classification anomaly detection large-scale dynamic networks integral image integral affinity graphs
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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