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 paper, we present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. 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-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 multi-round 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 graph neural network 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 datasets.
翻译:由于在一系列复杂的系统模型中广泛应用了可归属的网络,因此对可归属的网络进行异常的探测,吸引了相当多的研究兴趣。最近,深深深的学习基础异常的检测方法在浅浅的方法上,特别是在具有高维属性和复杂结构的网络上,显示出了有希望的结果。然而,现有的方法,即使用图形自动编码器作为主干线,并不充分利用网络的丰富信息,从而造成不尽人意的性能。此外,这些方法并不直接针对其学习目标中的异常的检测,而且由于完整的图表培训机制而未能推广到大型网络。为了克服这些局限性,我们在本文件中提出了一个新的对比式自我监督的自我监督的实验性实验性学习框架,用于在可归属的网络上检测异常现象。我们的框架充分利用了网络数据从新式对比式的本地信息,这种对比式的对比式对比式样能够以不超超强的方式捕捉到每个节点与其相邻的次结构之间的关系。同时,一个设计完善的图形神经网络对比式的对比式学习模型,从高层次的特性和地方结构中学习信息,并测量每个实例对准地对准的模型的对比,在每次的模型的测测测算的模型中,通过每次测算的测算的测算的测算的模型的模型的模型的测算法是不同的测算法,通过每个测算的测算的模型的测算的测算的测算的测算的测算的模型的测算法是每个特定的测算法。