Detecting abnormal patterns in call graphs based on the aggregation of relevant vertex measures

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Alves, R (Author) Other; Ferreira, P (Author) Other; Ribeiro, J (Author) Other; Belo, O (Author) Other

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 92-102

12th Industrial Conference on Advances in Data Mining, ICDM 2012

Berlin, 13 July 2012 through 20 July 2012

Indexing

Scopus - 1 Citation

Scientific classification

CORDIS: Technological sciences > Engineering > Knowledge engineering ; Technological sciences > Engineering > Computer engineering

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-008-5CH

DOI: 10.1007/978-3-642-31488-9_8

Abstract (EN): 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. © 2012 Springer-Verlag.

Language: English

Type (Professor's evaluation): Scientific