用于属性网络异常检测的重构增强多视图对比学习
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks
AI总结：
针对属性网络异常检测，本文提出联合优化多视图对比学习与掩码自编码器属性重构的自监督框架，并在五个基准上验证其优于现有方法。
摘要：
从属性网络中检测异常节点在金融欺诈检测和网络安全等许多实际应用中具有重要意义。由于异常节点与其他节点之间存在复杂的交互作用，并且它们在属性方面存在不一致性，因此这项任务颇具挑战性。本文提出一种自监督学习框架，通过联合优化基于多视图对比学习的模块和基于属性重构的模块，以更准确地检测属性网络上的异常。具体而言，首先建立两个对比学习视图，使模型能够更好地编码与异常相关的丰富局部和全局信息。受相邻节点之间属性一致性原则的启发，本文还引入了基于掩码自编码器的重构模块，用于识别具有较大重构误差的节点，并将这些节点视为异常。最后，将两个互补模块集成起来，以更准确地检测异常节点。在五个基准数据集上进行的大量实验表明，我们的模型优于当前最先进的模型。
英文摘要：
Detecting abnormal nodes from attributed networks is of great importance in many real applications, such as financial fraud detection and cyber security. This task is challenging due to both the complex interactions between the anomalous nodes with other counterparts and their inconsistency in terms of attributes. This paper proposes a self-supervised learning framework that jointly optimizes a multi-view contrastive learning-based module and an attribute reconstruction-based module to more accurately detect anomalies on attributed networks. Specifically, two contrastive learning views are firstly established, which allow the model to better encode rich local and global information related to the abnormality. Motivated by the attribute consistency principle between neighboring nodes, a masked autoencoder-based reconstruction module is also introduced to identify the nodes which have large reconstruction errors, then are regarded as anomalies. Finally, the two complementary modules are integrated for more accurately detecting the anomalous nodes. Extensive experiments conducted on five benchmark datasets show our model outperforms current state-of-the-art models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
