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.

中文翻译：

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谁是老大？通过集中性指导的深度随机游走识别电信欺诈网络中的关键角色

目的

电信（电信）欺诈是最常见的犯罪之一，并造成最大的经济损失。为了有效消除欺诈团伙，必须识别并捕获关键欺诈者。一种策略是使用社交网络分析来分析欺诈交互网络。但是，欺诈网络的底层结构与普通社交网络的结构不同，这使得诸如中心性之类的传统指标无法直接应用。最近，出现了一种新的研究路线，称为深度随机游走。这些方法利用随机游走探索局部信息，然后应用深度学习算法来学习代表性特征向量。尽管对于许多类型的网络都有效，但是随机游走用于发现局部结构等效性，并且不考虑节点的全局属性。

设计/方法/方法

作者提出了一种将深度随机游走与社会网络分析的优点相结合的新方法，称为中心指导深度随机游走。通过使用节点的中心性作为边缘权重，作者的偏向随机游动隐含地考虑了节点的全局重要性，因此可以更准确地找到关键的欺诈者角色。为了评估作者的算法，构建了一个具有约562名欺诈者的真实电信欺诈数据集，这是迄今为止最大的电信欺诈网络。

发现

作者提出的方法比传统的集中度指标和各种深度随机游走算法取得了更好的结果，并且成功地识别了欺诈网络中的关键角色。

研究局限/意义

该研究使用共同犯罪和逃跑记录来构建犯罪网络，将来可能会包含更多欺诈者的人际关系，例如友谊和亲戚。

创意/价值

本文提出了一种新的算法，以集中度为指导的深度随机游走，并将其应用于新的电信欺诈数据集。实验结果表明，作者的方法可以成功地识别欺诈组中的关键角色，并且胜过其他基准方法。据作者所知，这是迄今为止对电信欺诈网络的最大分析。