Detecting anomalous edges and nodes in dynamic networks is critical in various areas, such as social media, computer networks, and so on. Recent approaches leverage network embedding technique to learn how to generate node representations for normal training samples and detect anomalies deviated from normal patterns. However, most existing network embedding approaches learn deterministic node representations, which are sensitive to fluctuations of the topology and attributes due to the high flexibility and stochasticity of dynamic networks. In this paper, a stochastic neural network, named by Hierarchical Variational Graph Recurrent Autoencoder (H-VGRAE), is proposed to detect anomalies in dynamic networks by the learned robust node representations in the form of random variables. H-VGRAE is a semi-supervised model to capture normal patterns in training set by maximizing the likelihood of the adjacency matrix and node attributes via variational inference. Comparing with existing methods, H-VGRAE has three main advantages: 1) H-VGRAE learns robust node representations through stochasticity modeling and the extraction of multi-scale spatial-temporal features; 2) H-VGRAE can be extended to deep structure with the increase of the dynamic network scale; 3) the anomalous edge and node can be located and interpreted from the probabilistic perspective. Extensive experiments on four real-world datasets demonstrate the outperformance of H-VGRAE on anomaly detection in dynamic networks compared with state-of-the-art competitors.

中文翻译：

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H-VGRAE：一种用于动态网络中鲁棒异常检测的分层随机时空嵌入方法

检测动态网络中的异常边和节点在各个领域都至关重要，例如社交媒体、计算机网络等。最近的方法利用网络嵌入技术来学习如何为正常训练样本生成节点表示并检测偏离正常模式的异常。然而，大多数现有的网络嵌入方法学习确定性节点表示，由于动态网络的高度灵活性和随机性，这些表示对拓扑和属性的波动很敏感。在本文中，提出了一种名为分层变分图循环自动编码器（H-VGRAE）的随机神经网络，通过学习到的随机变量形式的鲁棒节点表示来检测动态网络中的异常。H-VGRAE 是一种半监督模型，通过变分推理最大化邻接矩阵和节点属性的可能性来捕获训练集中的正常模式。与现有方法相比，H-VGRAE 具有三个主要优点：1）H-VGRAE 通过随机建模和多尺度时空特征的提取来学习鲁棒的节点表示；2）随着动态网络规模的增加，H-VGRAE可以扩展到深层结构；3）可以从概率的角度定位和解释异常边缘和节点。对四个真实世界数据集的大量实验证明，与最先进的竞争对手相比，H-VGRAE 在动态网络中的异常检测方面表现出色。1）H-VGRAE通过随机建模和多尺度时空特征的提取来学习鲁棒的节点表示；2）随着动态网络规模的增加，H-VGRAE可以扩展到深层结构；3）可以从概率的角度定位和解释异常边缘和节点。对四个真实世界数据集的大量实验证明，与最先进的竞争对手相比，H-VGRAE 在动态网络中的异常检测方面表现出色。1）H-VGRAE通过随机建模和多尺度时空特征的提取来学习鲁棒的节点表示；2）随着动态网络规模的增加，H-VGRAE可以扩展到深层结构；3）可以从概率的角度定位和解释异常边缘和节点。对四个真实世界数据集的大量实验证明，与最先进的竞争对手相比，H-VGRAE 在动态网络中的异常检测方面表现出色。