论文标题
RESGCN：基于注意的归因网络异常检测的深度残差模型
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
论文作者
论文摘要
有效地检测归因网络中的异常淋巴结对于许多现实世界应用（例如欺诈和入侵检测）的成功至关重要。现有方法在三个主要问题上遇到困难：稀疏性和非线性捕获，残留建模和网络平滑。我们提出了剩余图形卷积网络（RESGCN），这是一种基于注意力的深度剩余建模方法，可以解决这些问题：使用GCN对属性网络进行建模允许捕获稀疏性和非线性；利用深层神经网络可以直接从输入中学习残留，而基于残留的注意机制可减少异常节点的不良影响，并防止过度光滑。在几个现实世界中归因网络的广泛实验证明了RESGCN在检测异常方面的有效性。
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.
