Do not ignore heterogeneity and heterophily: Multi-network collaborative telecom fraud detection

Abstract

The development of mobile communication technologies has enriched human interaction and inevitably led to the proliferation of telecom fraud. While recent studies have paid attention to collaborative fraud in telecom networks, they have examined only voice networks and neglected the collaborative fraud phenomenon in Short Message Service (SMS) networks and across voice and SMS networks. To address this issue, we investigated a telecom dataset with 5.01 million call records and 6.84 million SMS records between 6,106 users and 2.02 million recipients over eight months. On the one hand, we find differences in the evolution pattern of SMS behaviour between fraudsters and normal users. On the other hand, we also confirm the existence of intra-network collaborative fraud in voice networks and SMS networks and inter-network collaborative fraud across voice and SMS networks. To this end, we propose a novel telecom fraud detector, MLCG-FD, which models dynamic individual behaviours and multi-network collaborative relationships. First, a multi-network latent collaborative network (LGC) extractor is designed to extract two intra-network LCGs and one inter-network LCG from voice and SMS networks. Then, we design two behavioural encoders to encode individual voice and SMS behaviour patterns. Finally, a network-based hybrid-pass filter aggregates higher-order neighbour features from different networks. Experiments conducted on two real telecom datasets show that MLCG-FD significantly improves over state-of-the-art methods.