Graph Signal Processing on Dynamic Graphs

动态图上的图信号处理

• 批准号: 2768264
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2768264
• 关键词: Graph; Signal; Processing; Dynamic; Graphs

This PhD project is inline with EPSRC priority on artificial intelligence and robotics, aimed at developing novel learning methodologies for large scale networks. Networks occur frequently in real datasets such as social networks, e-commerce and financial transactions, in addition to various scientific and technological areas. With the rapidly increasing demand for analysing complex network-structured data, network science and network analysis has become one of the hottest research topics in the field of statistical data science and big data analytics. However, most statistical network analysis methods have been targeted at static networks, and the understanding of dynamic networks, a type of network constantly changes over time, is relatively poor. In view of the clear importance of dynamic networks, this project will build a theoretical framework for dynamic network analysis. It will involve metrics design, mathematical modeling and statistical analysis, as well as experimental validation. The research and validation will be carried out on publicly available datasets of human mobility and social networks. The ultimate goal of this work is to benefit the greatest number of people, so it will be utilized for several real-life problems, such as prediction of the spread of infectious diseases, analysis of social networks for recommendations (e.g. product or music recommendations) and prediction of demands in financial transactions.

这个博士项目与EPSRC在人工智能和机器人技术方面的优先事项相一致，旨在为大规模网络开发新的学习方法。网络经常出现在真实的数据集中，例如社交网络、电子商务和金融交易，以及各种科学和技术领域。随着对复杂网络结构数据分析需求的快速增长，网络科学和网络分析已成为统计数据科学和大数据分析领域最热门的研究课题之一。然而，大多数统计网络分析方法都是针对静态网络的，而动态网络，一种随时间不断变化的网络，其理解相对较差。鉴于动态网络的重要性，本项目将建立动态网络分析的理论框架。它将涉及指标设计，数学建模和统计分析，以及实验验证。研究和验证将在公开的人员流动和社交网络数据集上进行。这项工作的最终目标是使最多的人受益，因此它将被用于几个现实生活中的问题，例如预测传染病的传播，分析社交网络以进行推荐（例如产品或音乐推荐）以及预测金融交易中的需求。