Liu Yixin, Li Zhao, Pan Shirui, Gong Chen, Zhou Chuan, Karypis George
IEEE Trans Neural Netw Learn Syst. 2022 Jun;33(6):2378-2392. doi: 10.1109/TNNLS.2021.3068344. Epub 2022 Jun 1.
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this article, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network (GNN)-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multiround predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the GNN module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark data sets.
由于属性网络在建模各种复杂系统中具有广泛应用，因此属性网络上的异常检测吸引了大量研究兴趣。最近，基于深度学习的异常检测方法相较于浅层方法已显示出有前景的结果，特别是在具有高维属性和复杂结构的网络上。然而，现有的以图自动编码器为骨干的方法并未充分利用网络的丰富信息，导致性能欠佳。此外，这些方法在其学习目标中并未直接针对异常检测，并且由于全图训练机制而无法扩展到大型网络。为了克服这些限制，在本文中，我们提出了一种用于属性网络异常检测的新型对比自监督学习框架（简称为CoLA）。我们的框架通过对一种新型对比实例对进行采样来充分利用网络数据中的局部信息，这种实例对能够以无监督的方式捕捉每个节点与其相邻子结构之间的关系。同时，提出了一种精心设计的基于图神经网络（GNN）的对比学习模型，以从高维属性和局部结构中学习信息性嵌入，并通过其输出分数来衡量每个实例对的一致性。对比学习模型的多轮预测分数进一步用于通过统计估计来评估每个节点的异常性。通过这种方式，学习模型由一个特定的异常检测感知目标进行训练。此外，由于GNN模块的输入是批量的实例对而不是整个网络，我们的框架可以灵活地适应大型网络。实验结果表明，我们提出的框架在所有七个基准数据集上均优于当前的基线方法。
