ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
This work addresses anomaly detection for applications like fraud detection, but it appears incremental as it builds on existing GCN methods with residual modeling.
The paper tackled anomaly detection in attributed networks by proposing ResGCN, an attention-based deep residual modeling approach, which achieved effective results as demonstrated in experiments on real-world datasets.
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity; utilizing a deep neural network allows to directly learn residual from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies.