Targeted Graph Embedding for Anomaly Detection in Large-scale Networks

该项目旨在通过利用图嵌入技术来解决大规模网络中异常检测的挑战性问题。它希望提供一系列创新的图形嵌入算法，以优化异常检测。通过解决欠发达的研究挑战，如异常的多样性类型和缺乏异常标签，建立的理论和设计的方法将推进图异常检测和图表示学习的前沿技术。通过高效、准确地发现异常，该项目将有助于从虚假评论检测到金融欺诈识别的多个真实的应用，带来社会效益和经济效益。

This project aims to tackle the challenging problem of anomaly detection in large-scale networks by leveraging graph embedding techniques. It expects to deliver a series of innovative graph embedding algorithms targeting optimised anomaly detection. By addressing under-developed research challenges, such as the versatile types of anomalies and lack of anomaly labels, the established theories and devised methodologies will advance frontier technologies in both graph anomaly detection and graph representation learning. By uncovering anomalies with high efficiency and accuracy, this project will contribute to multiple real applications from fake review detection to financial fraud identification, bringing both social and economic benefits.