GRFA: Leveraging Adaptive Graph Reconstruction for Fake Account Detection in Social Networks

抄録

The proliferation of fake accounts in social networks has prompted growing attention to the development of effective detection techniques for ensuring cyberspace security. These fake accounts frequently employ sophisticated camouflage strategies to evade detection, which compromises the reliability of local neighborhood information. We propose GRFA, a novel approach for fake account detection that incorporates similarity-based adaptive graph reconstruction. The framework introduces a reinforcement learning-based adaptive mechanism to construct similarity edges, which dynamically refines the graph structure to better capture global dependencies. These refined structures are then incorporated into a heterogeneous graph neural network with dual aggregation, significantly improving the detection of camouflaged accounts. Experimental results demonstrate that GRFA outperforms state-of-the-art methods across multiple real-world datasets.