Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user’s behavior, and texts attached to individual transactions and the user. However, this traditional approach may be limited because malicious users can easily conceal their information. Given this background, network indices have been exploited for detecting frauds in various online transaction platforms. In the present study, we analyzed networks of users of an online consumer-to-consumer marketplace in which a seller and the corresponding buyer of a transaction are connected by a directed edge. We constructed egocentric networks of each of several hundreds of fraudulent users and those of a similar number of normal users. We calculated eight local network indices based on up to connectivity between the neighbors of the focal node. Based on the present descriptive analysis of these network indices, we fed twelve features that we constructed from the eight network indices to random forest classifiers with the aim of distinguishing between normal users and fraudulent users engaged in each one of the four types of problematic transactions. We found that the classifier accurately distinguished the fraudulent users from normal users and that the classification performance did not depend on the type of problematic transaction.

在线市场的提供者一直在与一些用户进行的有问题的交易作斗争，例如出售非法物品和张贴虚构物品。检测欺诈活动的一种典型方法是分析注册的用户个人资料，用户的行为以及附加到单个交易和用户的文本。但是，这种传统方法可能会受到限制，因为恶意用户可以轻松隐藏其信息。在这种背景下，网络索引已被用于检测各种在线交易平台中的欺诈行为。在本研究中，我们分析了在线消费者对消费者市场的用户网络，在该网络中，交易的卖方和相应的买方通过有向边连接。我们构建了数百个欺诈用户和相似数量的普通用户各自的以自我为中心的网络。我们根据焦点节点邻居之间的连通性计算了八个本地网络索引。基于对这些网络索引的当前描述性分析，我们将从八个网络索引构建的十二种特征提供给随机森林分类器，目的是区分从事四种类型的有问题交易中的每一种的正常用户和欺诈用户。我们发现分类器准确地将欺诈用户与正常用户区分开，并且分类性能不取决于问题交易的类型。我们根据焦点节点邻居之间的连通性计算了八个本地网络索引。基于对这些网络索引的当前描述性分析，我们将从八个网络索引构建的十二种特征提供给随机森林分类器，目的是区分从事四种类型的有问题交易中的每一种的正常用户和欺诈用户。我们发现分类器准确地将欺诈用户与正常用户区分开，并且分类性能不取决于问题交易的类型。我们根据焦点节点邻居之间的连通性计算了八个本地网络索引。基于对这些网络索引的当前描述性分析，我们将从八个网络索引构建的十二种特征提供给随机森林分类器，目的是区分从事四种类型的有问题交易中的每一种的正常用户和欺诈用户。我们发现分类器准确地将欺诈用户与正常用户区分开，并且分类性能不取决于问题交易的类型。我们将从八个网络索引构建的十二种功能馈送到随机森林分类器，目的是区分从事四种有问题交易的每一种的正常用户和欺诈用户。我们发现分类器准确地将欺诈用户与正常用户区分开，并且分类性能不取决于问题交易的类型。我们将从八个网络索引构建的十二种功能馈送到随机森林分类器，目的是区分从事四种有问题交易的每一种的正常用户和欺诈用户。我们发现分类器准确地将欺诈用户与正常用户区分开，并且分类性能不取决于问题交易的类型。