Spectral embedding methods and subsequent inference tasks on dynamic multiplex graphs
动态多路复用图上的谱嵌入方法和后续推理任务
基本信息
- 批准号:EP/Y002113/1
- 负责人:
- 金额:$ 20.94万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2024
- 资助国家:英国
- 起止时间:2024 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The proposed research is centred around statistical analysis of dynamic multiplex graphs (DMPGs). Mathematically, a graph, also known as network, can be interpreted as a collection of nodes, with edges occurring between them. Network data are collected in many domains, such as healthcare, biology, and cyber-security, and they are becoming increasingly rich, continuously generating new research questions. In particular, dynamic multiplex networks are emerging as increasingly common data structures observed in real-world applications. In DMPGs, edges could have different types, and evolve in time. For example, in an enterprise computer network, nodes could be represented by hosts, and edges correspond to connections between them, occurring dynamically over time on different ports. Because of the complexity of such objects, research has only scratched the surface with statistical modelling for DMPGs. Therefore, the development of novel statistical methodology is required, and this research intends to bridge this gap, developing realistic statistical models for DMPGs.The aim of this research proposal is to develop principled and scalable statistical models which represent the full multi-layered complexity of dynamic multiplex graphs. This goal will be achieved by exploiting an array of statistical techniques, spanning from spectral methods to topic modelling. In particular, this research proposal focusses on techniques for discovering low-dimensional substructure in networks, known as embedding methods. Such techniques have the added benefit of aiding subsequent inference tasks, such as clustering of nodes with similar behaviour. The statistical properties of novel embedding methods proposed for DMPGs will be carefully assessed, and the proposed methods will be utilised to improve and extend existing models for clustering, link prediction, and anomaly detection. In addition, the proposed models will have the flexibility to encompass additional information on nodes and edges, available in the form of covariates. In particular, this research proposal will focus on incorporating unstructured data, such as text, within the proposed modelling frameworks, combining aspects from network analysis and natural language processing.
拟议的研究是围绕动态多重图(DMPG)的统计分析。在数学上,一个图,也被称为网络,可以被解释为一个节点的集合,它们之间有边。网络数据收集在许多领域,如医疗保健,生物学和网络安全,它们变得越来越丰富,不断产生新的研究问题。特别是,动态多路复用网络正在成为现实世界中观察到的越来越常见的数据结构。在DMPG中,边可以具有不同的类型,并且随着时间的推移而演变。例如,在企业计算机网络中,节点可以由主机表示,边缘对应于它们之间的连接,随着时间的推移在不同端口上动态发生。由于这类物体的复杂性,研究人员对DMPG的统计建模只触及了表面。因此,新的统计方法的发展是必需的,本研究旨在弥合这一差距,发展现实的统计模型DMPGs.The研究建议的目的是开发原则和可扩展的统计模型,代表了完整的多层复杂的动态复用图。这一目标将通过利用一系列统计技术来实现,从光谱方法到主题建模。特别是,这项研究计划的重点是在网络中发现低维子结构的技术,称为嵌入方法。这样的技术具有辅助后续推理任务的额外益处,例如具有相似行为的节点的聚类。将仔细评估为DMPG提出的新型嵌入方法的统计特性,并将利用提出的方法来改进和扩展现有的聚类、链接预测和异常检测模型。此外,所提出的模型将具有灵活性,以包含有关节点和边的额外信息,这些信息以协变量的形式提供。特别是,这项研究提案将侧重于将文本等非结构化数据纳入拟议的建模框架,结合网络分析和自然语言处理的各个方面。
项目成果
期刊论文数量(0)
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Francesco Sanna Passino其他文献
Online Bayesian changepoint detection for network Poisson processes with community structure
具有社区结构的网络泊松过程的在线贝叶斯变点检测
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Joshua Corneck;Edward A. K. Cohen;James S. Martin;Francesco Sanna Passino - 通讯作者:
Francesco Sanna Passino
Where To Next? A Dynamic Model of User Preferences
下一步去哪里?
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:0
- 作者:
Francesco Sanna Passino;Lucas Maystre;Dmitrii Moor;Ashton Anderson;M. Lalmas - 通讯作者:
M. Lalmas
Mutually Exciting Point Process Graphs for Modeling Dynamic Networks
用于动态网络建模的互激点流程图
- DOI:
- 发表时间:
2021 - 期刊:
- 影响因子:2.4
- 作者:
Francesco Sanna Passino;N. Heard - 通讯作者:
N. Heard
Graph‐based mutually exciting point processes for modelling event times in docked bike‐sharing systems
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- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Francesco Sanna Passino;Yining Che;Carlos Cardoso Correia Perello - 通讯作者:
Carlos Cardoso Correia Perello
Francesco Sanna Passino的其他文献
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