tcc2vec: RFM-informed representation learning on call graphs for churn prediction

Applying social network analytics for telco churn prediction has become indispensable for almost a decade. However, in the current literature, the uptake does not reflect in a significantly increased leverage of the available information that these networks convey. First, network featurization in general is a very cumbersome process due to the complex nature of networks and the lack of a respective methodology.This results in ad hoc approaches and hand-crafted features. Second, derivingcertain structural features in very large graphs is computationally expensive and,as a consequence, often neglected. Third, call networks are mostly treated as static in spite of their inherently dynamic nature. In this study, we propose tcc2vec, a panoptic approach aiming at devising representation learning (to address the first problem) on enriched call networks that integrate interaction and structural information (to overcome the second problem), which are being sliced in different time periods in order to account for different temporal granularities (hence addressing the third problem). In an extensive experimental analysis, insights are provided regarding an optimal choice of interaction and temporal granularities, as well as representation learning parameters