The telecommunication industry is a saturated market where a proper implementation of a retention campaign is critical to be competitive, since retaining a customer is cheaper than attracting a new one. Hence, it is crucial to detect customer behavioral patterns and define accurate approaches to predict potential churners. Multiple researchers have used binary classification methods to predict churn of customers. Some of them verify that customers’ social relationships influence the decision of changing the operator.

We propose a novel method to extract the dynamic relevance of each customer using social network analysis techniques with a binary classification method called similarity forests. The dynamic importance of each customer is determined by applying various centrality metrics over temporal graphs, to represent the relationships between customers and to extract behavioral patterns of churners and non-churners. These relationships are established in a temporal graph using the call detail records (CDR) of telco’s customers. In this paper, we compare the performance of different centrality metrics applied over two types of temporal graphs: Time-Order Graph and Aggregated Static Graph.

电信行业是一个饱和的市场，在这个市场上，正确实施保留活动对于提高竞争力至关重要，因为保留客户比吸引新客户便宜。因此，检测客户行为模式并定义准确的方法来预测潜在客户流失至关重要。众多研究人员已使用二进制分类方法来预测客户流失率。他们中的一些人证明客户的社会关系会影响更换运营商的决定。

我们提出了一种新颖的方法，该方法使用社交网络分析技术和称为相似性森林的二进制分类方法来提取每个客户的动态相关性。每个客户的动态重要性是通过在时间图上应用各种中心度度量标准来确定的，以表示客户之间的关系并提取搅局者和非搅局者的行为模式。这些关系是使用电信公司客户的呼叫详细记录（CDR）在时间图中建立的。在本文中，我们比较了在两种类型的时间图上应用的不同中心度度量的性能：时间顺序图和聚合静态图。