Residual-enhanced graph convolutional networks with hypersphere mapping for anomaly detection in attributed networks

In the burgeoning field of anomaly detection within attributed networks, traditional methodologies often encounter the intricacies of network complexity, particularly in capturing nonlinearity and sparsity. This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism, thereby creating a more nuanced and efficient method for anomaly detection in complex networks. The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data. This is further bolstered by deep residual learning, which is employed to model intricate nonlinear connections directly from input data. A pivotal innovation in our approach is the incorporation of a residual-based attention mechanism. This mechanism dynamically adjusts the importance of nodes based on their residual information, thereby significantly enhancing the sensitivity of the model to subtle anomalies. Furthermore, we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data. This mapping is the key to our model's ability to pinpoint anomalies with greater precision. An extensive experimental setup was used to validate the efficacy of the proposed model. Using attributed social network datasets, we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection. The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.