Network Stochastic Processes and Time Series (NeST)

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，无论是在暗网上发现严重的犯罪，入侵计算机网络，还是在全球互联网范围内劫持，网络安全都迫切需要更好的网络异常检测工具。描述在大脑不同位置记录的多个EEG时间序列的网络结构对于理解神经系统疾病和治疗方法的发展至关重要。动态网络建模在交通运输应用中有很大的兴趣，例如防止高速公路事故和预测恶劣天气对铁路网络的影响。系统地识别、归因和防止在线错误信息需要社会网络中信息流的现实模型。虽然简单的随机网络理论在数学和计算机科学中已经建立起来，但最近动态网络数据的爆炸式增长暴露了我们处理现实生活网络的能力存在很大差距。经典的网络模型已经产生了一系列漂亮的数学理论，但并不总是能捕捉到真实数据中的丰富结构和时间动态，也不能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计划将开发健壮的、有原则的、但在计算上可行的方法来模拟动态变化的网络及其统计过程。这些问题的某些方面，例如量化政策干预对错误信息或疾病传播的影响，需要在概率论方面取得进展。动态网络数据也是出了名的难以分析。在计算层面，数据集通常非常大，并且/或者只能在“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，甚至理解数据以及它们与分析目标的关系都是具有挑战性的。因此，为了以系统的方式解决这些研究问题，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。在为期6年的项目中，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6个世界级研究所合作，开展领先和有影响力的研究。在不同的重叠组中，我们将解决以下问题：我们如何建模数据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数据分析，或者引用一位著名统计学家的话，“看数据，看看它似乎在说什么”（Tukey, 1977）？我们如何预测网络数据，或检测异常、变化和趋势？为了将技术应用于实践，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，为许多关键科学学科的挑战提供信息和驱动。有价值的工作成果将是来自广泛应用领域的高质量、精心策划的动态网络数据集，我们将在存储库中公开提供基准测试、测试和可重复性（负责任的创新），部分作为促进新合作的工具。我们还制定了一项战略，通过各种各样的科学出版途径、高质量的免费软件（例如R软件包、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还将在本地区精心培育和发展下一代训练有素、研究活跃的人才，这将有力地满足工业、政府和学术界对这类人才的高需求。

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.