ATD: Collaborative Research: Real-Time Network Pattern Change Detection

ATD：协作研究：实时网络模式变化检测

• 批准号: 1924859
• 负责人: Jie Yang
• 金额: $ 4.98万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924859&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Real; Time

The rapidly booming amounts of social networks data from the Internet offers a lot of information to understand human behaviors. First, the networks data contains sparse communication frequencies and some dense clusters, and the clusters change over time, so that feature generation and selection are essential. This research project addresses the statistical challenges for detecting abrupt categories changes in networks. This is important for quantifying human dynamics and accurately identifying unusual events and forecast future threats indicated by those events. Graduate students will be involved in some aspects of the project.This project aims to develop 1) for the static case: we will use zero-inflated or hurdle models to characterize the class link probability. 2) for the dynamic case: the class communication probability is a variable of time, we model the probability by a self-exciting process. 3) we consider the cold-start problem in which the predicted networks vary a lot from the training network, so that there are no enough samples to train classification models. Instead, we will develop matrix-variate clustering and classification models. This project includes several important topics to improve modeling of the network users' categories and identifying efficiently abrupt network pattern changes in real time as well as reducing the influence of outliers. These methods are applicable to various types of networks data such as social networks, biology signals, genome sequences, and so on. The PIs will provide a publicly-available software packages to implement the proposed methods. Additionally, corresponding statistical theories and computational techniques can be extended to advance further research and can be applied to other fields. This project topics cater to the students with hands-on studies in new Big-Data analysis program at the University of Central Florida.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

来自互联网的社交网络数据迅速蓬勃发展，提供了许多信息来了解人类行为。首先，网络数据包含稀疏的通信频率和一些密集的簇，并且簇随时间变化，因此特征生成和选择至关重要。该研究项目解决了检测网络突然类别变化的统计挑战。这对于量化人类动态并准确识别这些事件所指示的异常事件和预测未来威胁很重要。研究生将参与该项目的某些方面。此项目旨在开发1）静态案例：我们将使用零充气或跨栏模型来表征类链接概率。 2）对于动态情况：类通信概率是时间的变量，我们通过自我激发过程对概率进行建模。 3）我们认为预测网络与培训网络有很大差异的冷门问题，因此没有足够的样本来培训分类模型。相反，我们将开发矩阵变化的聚类和分类模型。该项目包括几个重要主题，以改善网络用户类别的建模并确定实时突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然突然使用异常值的影响。这些方法适用于各种类型的网络数据，例如社交网络，生物学信号，基因组序列等。 PI将提供一个公开可用的软件包来实现所提出的方法。此外，可以扩展相应的统计理论和计算技术以进一步研究，并可以应用于其他领域。该项目主题迎合了中央佛罗里达大学新的大数据分析计划的动手研究的学生。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。

项目成果

An R package AZIAD for analysing zero-inflated and zero-altered data

• DOI: 10.1080/00949655.2023.2207020
• 发表时间: 2023-11-02
• 期刊: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
• 影响因子: 1.2
• 作者: Mousavi, Niloufar Dousti;Aldirawi, Hani;Yang, Jie
• 通讯作者: Yang, Jie

Affine-transformation invariant clustering models

• DOI: 10.1186/s40488-020-00111-y
• 发表时间: 2020-10
• 期刊: Journal of Statistical Distributions and Applications
• 作者: Hsin-Hsiung Huang;Jie Yang

Score-matching representative approach for big data analysis with generalized linear models

• DOI: 10.1214/21-ejs1965
• 发表时间: 2018-07
• 期刊: Electronic Journal of Statistics
• 影响因子: 1.1
• 作者: Keren Li;Jie Yang

Modeling Sparse Data Using MLE with Applications to Microbiome Data

• DOI: 10.1007/s42519-021-00230-y
• 发表时间: 2022-03-01
• 期刊: JOURNAL OF STATISTICAL THEORY AND PRACTICE
• 影响因子: 0.6
• 作者: Aldirawi, Hani;Yang, Jie
• 通讯作者: Yang, Jie

Identifying zero-inflated distributions with a new R package iZID

使用新的 R 包 iZID 识别零膨胀分布

• DOI: 10.4310/cis.2020.v20.n1.a2
• 发表时间: 2020
• 期刊: Communications in Information and Systems
• 影响因子: 0.9
• 作者: Wang, Lei;Aldirawi, Hani;Yang, Jie
• 通讯作者: Yang, Jie