  With the rapid development of mobile networks, the people's social contacts
have been considerably facilitated. However, the rise of mobile social network
fraud upon those networks, has caused a great deal of distress, in case of
depleting personal and social wealth, then potentially doing significant
economic harm. To detect fraudulent users, call detail record (CDR) data, which
portrays the social behavior of users in mobile networks, has been widely
utilized. But the imbalance problem in the aforementioned data, which could
severely hinder the effectiveness of fraud detectors based on graph neural
networks(GNN), has hardly been addressed in previous work. In this paper, we
are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by
creatively combining cost-sensitive learning and graph neural networks. We
conduct extensive experiments on two open-source realworld mobile network fraud
datasets. The results show that CSGNN can effectively solve the graph imbalance
problem and then achieve better detection performance than the state-of-the-art
algorithms. We believe that our research can be applied to solve the graph
imbalance problems in other fields. The CSGNN code and datasets are publicly
available at https://github.com/xxhu94/CSGNN.
