From Classic GNNs to Hyper-GNNs for Detecting Camouflaged Malicious Actors

Abstract

Graph neural networks (GNNs), which extend deep learning models to graph-structured data, have achieved great success in many applications such as detecting malicious activities. However, GNN-based models are vulnerable to camouflage behavior of malicious actors, i.e., the performance of existing GNN-based models has been hindered significantly. In this research proposal, we follow two research directions to address this challenge. One direction focuses on enhancing the existing GNN-based models and enabling them to identify both camouflaged and non-camouflaged malicious actors. In this regard, we propose to explore an adaptive aggregation strategy, which empowers GNN-based models to handle camouflage behavior of fraudsters. The other research direction concentrates on leveraging hypergraph neural networks (hyper-GNNs) to learn nodes' representation for more effective identification of camouflaged malicious actors.