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 direct residual ing 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.

有效检测属性网络中的异常节点对于欺诈和入侵检测等许多实际应用的成功至关重要。现有方法在三个主要问题上存在困难：稀疏性和非线性捕获、残差建模和网络平滑。我们提出了残差图卷积网络 (ResGCN)，这是一种基于注意力的深度残差建模方法，可以解决这些问题：使用 GCN 对属性网络建模允许捕获稀疏性和非线性，利用深度神经网络允许从输入中直接残差，基于残差的注意力机制减少了异常节点的不利影响并防止过度平滑。对几个真实世界属性网络的大量实验证明了 ResGCN 在检测异常方面的有效性。