Network Stochastic Processes and Time Series (NeST)

网络随机过程和时间序列 (NeST)

基本信息

  • 批准号:
    EP/X002195/1
  • 负责人:
  • 金额:
    $ 657.67万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 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

项目摘要

Dynamic networks occur in many fields of science, technology and medicine, as well as everyday life. Understanding their behaviour has important applications. For example, whether it is to uncover serious crime on the dark web, intrusions in a computer network, or hijacks at global internet scales, better network anomaly detection tools are desperately needed in cyber-security. Characterising the network structure of multiple EEG time series recorded at different locations in the brain is critical for understanding neurological disorders and therapeutics development. Modelling dynamic networks is of great interest in transport applications, such as for preventing accidents on highways and predicting the influence of bad weather on train networks. Systematically identifying, attributing, and preventing misinformation online requires realistic models of information flow in social networks.Whilst simple random networks theory is well-established in maths and computer science, the recent explosion of dynamic network data has exposed a large gap in our ability to process real-life networks. Classical network models have led to a body of beautiful mathematical theory, but do not always capture the rich structure and temporal dynamics seen in real data, nor are they geared to answer practitioners' typical questions, e.g. relating to forecasting, anomaly detection or data ethics issues. Our NeST programme will develop robust, principled, yet computationally feasible ways of modelling dynamically changing networks and the statistical processes on them.Some aspects of these problems, such as quantifying the influence of policy interventions on the spread of misinformation or disease, require advances in probability theory. Dynamic network data are also notoriously difficult to analyse. At a computational level, the datasets are often very large and/or only available "on the stream". At a statistical level, they often come with important collection biases and missing data. Often, even understanding the data and how they may relate to the analysis goal can be challenging. Therefore, to tackle these research questions in a systematic way we need to bring probabilists, statisticians and application domain experts together.NeST's six-year programme will see probabilists and statisticians with theoretical, computational, machine learning and data science expertise, collaborate across six world-class institutes to conduct leading and impactful research. In different overlapping groups, we will tackle questions such as: How do we model data to capture the complex features and dynamics we observe in practice? How should we conduct exploratory data analysis or, to quote a famous statistician, "Looking at the data to see what it seems to say" (Tukey, 1977)? How can we forecast network data, or detect anomalies, changes, trends? To ground techniques in practice, our research will be informed and driven by challenges in many key scientific disciplines through frequent interaction with industrial & government partners in energy, cyber-security, the environment, finance, logistics, statistics, telecoms, transport, and biology. A valuable output of work will be high-quality, curated, dynamic network datasets from a broad range of application domains, which we will make publicly available in a repository for benchmarking, testing & reproducibility (responsible innovation), partly as a vehicle to foster new collaborations. We also have a strategy to disseminate knowledge through a diverse range of scientific publication routes, high-quality free software (e.g. R packages, Python notebooks accompanying data releases), conferences, patents and outreach activities. NeST will also carefully nurture and develop the next generation of highly-trained and research-active people in our area, which will contribute strongly to satisfying the high demand for such people in industry, government and academia.
动态网络出现在科学、技术和医学的许多领域,以及日常生活中。了解它们的行为具有重要的应用价值。例如,无论是揭露黑暗网络上的严重犯罪,还是对计算机网络的入侵,还是全球互联网规模的劫持,网络安全都迫切需要更好的网络异常检测工具。表征在大脑不同位置记录的多个脑电时间序列的网络结构对于理解神经疾病和治疗发展至关重要。动态网络模型在交通应用中具有重要意义,如预防高速公路交通事故、预测恶劣天气对列车网络的影响等。系统地识别、归属和防止在线错误信息需要社会网络中的信息流的现实模型。虽然简单的随机网络理论在数学和计算机科学中已经很好地建立了,但最近动态网络数据的爆炸暴露了我们处理现实生活网络的能力的巨大差距。经典网络模型产生了一系列漂亮的数学理论,但并不总是捕捉到真实数据中看到的丰富的结构和时间动态,也不适合回答从业者的典型问题,例如与预测、异常检测或数据伦理问题有关的问题。我们的Nest计划将开发稳健、有原则但在计算上可行的方法,对动态变化的网络和它们上的统计过程进行建模。这些问题的某些方面,如量化政策干预对错误信息或疾病传播的影响,需要概率论的进步。动态网络数据也是出了名的难以分析。在计算级别上,数据集通常非常大和/或仅在流上可用。在统计层面上,它们往往伴随着重要的收集偏差和数据缺失。通常,即使理解数据以及它们可能如何与分析目标相关联也是具有挑战性的。因此,为了系统地解决这些研究问题,我们需要将概率学家、统计学家和应用领域的专家聚集在一起。在NeST的六年计划中,拥有理论、计算、机器学习和数据科学专业知识的概率学家和统计学家将在六个世界级研究所合作,进行领先和有影响力的研究。在不同的重叠小组中,我们将解决这样的问题:我们如何对数据进行建模,以捕获我们在实践中观察到的复杂特征和动态?我们应该如何进行探索性的数据分析,或者引用一位著名统计学家的话说,“看看数据,看看它似乎说了什么”(Tukey,1977)?我们如何预测网络数据,或检测异常、变化和趋势?为了将技术付诸实践,我们的研究将通过与能源、网络安全、环境、金融、物流、统计、电信、运输和生物等领域的工业和政府合作伙伴的频繁互动,了解并推动许多关键科学学科的挑战。有价值的工作成果将是来自广泛应用领域的高质量、经过管理的动态网络数据集,我们将在存储库中公开提供这些数据集,用于基准、测试和重现性(负责任的创新),部分原因是作为促进新合作的工具。我们还制定了一项战略,通过各种科学出版途径、高质量的自由软件(例如,R包、随数据发布的Python笔记本)、会议、专利和推广活动来传播知识。Nest还将精心培养和发展我们地区训练有素、积极从事研究的下一代人才,这将为满足工业界、政府和学术界对这类人才的高需求做出强有力的贡献。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Spectral Embedding of Weighted Graphs
  • DOI:
    10.1080/01621459.2023.2225239
  • 发表时间:
    2019-10
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Ian Gallagher;Andrew Jones;A. Bertiger;C. Priebe;Patrick Rubin-Delanchy
  • 通讯作者:
    Ian Gallagher;Andrew Jones;A. Bertiger;C. Priebe;Patrick Rubin-Delanchy
Adaptive wavelet domain principal component analysis for nonstationary time series
非平稳时间序列的自适应小波域主成分分析
Statistical Cybersecurity: A Brief Discussion of Challenges, Data Structures, and Future Directions
统计网络安全:挑战、数据结构和未来方向的简要讨论
  • DOI:
    10.1162/99608f92.240383c7
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sanna Passino F
  • 通讯作者:
    Sanna Passino F
Changepoint Detection on a Graph of Time Series
时间序列图上的变化点检测
  • DOI:
    10.1214/23-ba1365
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Hallgren K
  • 通讯作者:
    Hallgren K
Autoregressive Networks
  • DOI:
  • 发表时间:
    2020-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Binyan Jiang;Jialiang Li;Q. Yao
  • 通讯作者:
    Binyan Jiang;Jialiang Li;Q. Yao
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Guy Nason其他文献

Guy Nason的其他文献

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{{ truncateString('Guy Nason', 18)}}的其他基金

Locally Stationary Time Series and Multiscale Methods for Statistics (LuSTruM)
局部平稳时间序列和多尺度统计方法 (LuSTruM)
  • 批准号:
    EP/K020951/1
  • 财政年份:
    2013
  • 资助金额:
    $ 657.67万
  • 项目类别:
    Fellowship
Locally stationary Energy Time Series (LETS)
局部固定能量时间序列 (LETS)
  • 批准号:
    EP/I01697X/1
  • 财政年份:
    2011
  • 资助金额:
    $ 657.67万
  • 项目类别:
    Research Grant

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    青年科学基金项目

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Large Graph Limits of Stochastic Processes on Random Graphs
随机图上随机过程的大图极限
  • 批准号:
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  • 财政年份:
    2024
  • 资助金额:
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Stochastic processes in random environments with inhomogeneous scaling limits
具有不均匀缩放限制的随机环境中的随机过程
  • 批准号:
    24K06758
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    2024
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Spectral theory of Schrodinger forms and Stochastic analysis for weighted Markov processes
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  • 财政年份:
    2023
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Conference: Seminar on Stochastic Processes 2023
会议:随机过程研讨会 2023
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    2244835
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    2023
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    $ 657.67万
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    Standard Grant
Stochastic processes on random graphs with clustering
具有聚类的随机图上的随机过程
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    EP/W033585/1
  • 财政年份:
    2023
  • 资助金额:
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随机图上的随机函数和随机过程
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Stochastic processes in sub-Riemannian geometry
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Optimal Transport of Stochastic Processes in Mathematical Finance
数学金融中随机过程的最优传输
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Applications of stochastic analysis to statistical inference for stationary and non-stationary Gaussian processes
随机分析在平稳和非平稳高斯过程统计推断中的应用
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随机过程研讨会 2022
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