Unveiling camouflaged malicious accounts via unsupervised behavior-affinity dual-graph learning

Detecting malicious accounts on social media is becoming increasingly challenging as adversaries evolve from simple spammers to sophisticated actors capable of camouflaging their network topology. Existing unsupervised detection methods predominantly rely on explicit follower/followee structures. However, these topological signals are often manipulated by attackers (e.g., through link farming), compromising the reliability of structure-based Graph Neural Networks (GNNs) and obscuring the latent