Targeted Graph Embedding for Anomaly Detection in Large-scale Networks

用于大规模网络中异常检测的目标图嵌入

• 批准号: DP210101347
• 负责人: Prof Ling Chen
• 金额: $ 25.95万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-01-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210101347
• 关键词: Targeted; Graph; Embedding; Anomaly; Detection

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.

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。它期望提供一系列针对优化异常检测的创新图嵌入算法。通过解决尚未开发的研究挑战，例如异常类型的多样性和异常标签的缺乏，已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方面的前沿技术。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等多种实际应用，带来社会效益和经济效益。