摘要:动态变化的图数据在现实应用中广泛存在，有效地对动态网络异常数据进行挖掘，具有重要的科学价值和实践意义.大多数传统的动态网络异常检测算法主要关注于网络结构的异常，而忽视了节点和边的属性以及网络变化的作用.提出一种基于图神经网络的异常检测算法，将图结构、属性以及动态变化的信息引入模型中，来学习进行异常检测的表示向量.具体地，改进图上无监督的图神经网络框架DGI，提出一种面向动态网络无监督表示学习算法Dynamic-DGI.该方法能够同时提取网络本身的异常特性以及网络变化的异常特性，用于表示向量的学习.实验结果表明，使用该算法学得的网络表示向量进行异常检测，得到的结果优于最新的子图异常检测算法SpotLight，并且显著优于传统的网络表示学习算法.除了能够提升异常检测的准确度，该算法也能够挖掘网络中存在的有实际意义的异常.



Abstract:Dynamic graph structured data is ubiquitous in real-life applications. Mining outliers on dynamic networks is an important problem, which is very useful for many practical applications. Most traditional network outlier detection algorithms focus mainly on the strutraulal anomaly, ignoring the nodes and edges' attributes, and the time-varying features as well. This study proposes a graph neural network based network anomaly detection algorithm which can capture the nodes and edges' attributes and time-varying features and fully uses these features to learn a representation vector for each node. Specifically, the proposed algorithm improves an unsupervised graph neural network framework called DGI. Based on DGI, a new danamic DGI algorithm is proposed, which is called Dynamic-DGI, for dynamic networks. Dynamic-DGI can simultaneously extracts the abnormal characteristics of the network itself and the abnormal characteristics of the network changes. The experimental results show that the proposed algorithm is better than the state-of-the-art anomaly detection algorithm SpotLight, and is significantly better than the traditional network representation learning algorithms. In addition to improving the accuracy, the proposed algorithmis also able to mine interesting anomalies in the network.



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