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Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

Yi-Chun Chang (Department of Information Management, National Taiwan University, Taipei, Taiwan)
Kuan-Ting Lai (Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan)
Seng-Cho T. Chou (Department of Information Management, National Taiwan University, Taipei, Taiwan)
Wei-Chuan Chiang (Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan)
Yuan-Chen Lin (Anti-Fraud Center, Criminal Investigation Bureau, Taipei, Taiwan)

Data Technologies and Applications

ISSN: 2514-9288

Article publication date: 4 November 2020

Issue publication date: 13 January 2021

277

Abstract

Purpose

Telecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.

Design/methodology/approach

The authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.

Findings

The authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.

Research limitations/implications

The study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.

Originality/value

This paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

Keywords

Citation

Chang, Y.-C., Lai, K.-T., Chou, S.-C.T., Chiang, W.-C. and Lin, Y.-C. (2021), "Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk", Data Technologies and Applications, Vol. 55 No. 1, pp. 1-18. https://doi.org/10.1108/DTA-05-2020-0103

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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