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CAREER: Inferring Graph Structure via Spectral Representations of Network Processes

职业：通过网络过程的频谱表示推断图结构

基本信息

批准号：
1750428
负责人：
Gonzalo Mateos Buckstein
金额：
$ 40.79万
依托单位：
University of Rochester
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2023-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750428&HistoricalAwards=false
关键词：
CAREER Inferring Graph Structure via

项目摘要

Coping with the challenges found at the intersection of Network Science and Big Data necessitates fundamental breakthroughs in modeling, identification, and controllability of distributed network processes -- often conceptualized as signals defined on graphs. There is an evident mismatch between the scientific understanding of signals defined over regular domains (time or space) and graph-valued signals. Knowledge about time series was developed over the course of decades and boosted by real needs in areas such as communications, speech, or control. On the contrary, the prevalence of network-related signal processing problems and the access to quality network data are recent events. In this context, research in this project aims to push the frontiers of knowledge in network-analytic information processing, and thus make progress towards understanding the inherent complexities of strongly coupled systems such as the brain. Students will also be trained to tackle the problems at the intersection of Big Data and Network Science, thereby contributing to workforce development as well.Under the assumption that the signals are related to the topology of the graph where they are supported, the goal of graph signal processing is to develop algorithms that fruitfully leverage this relational structure, and can make inferences about these relationships when they are only partially observed. Most graph signal processing efforts to date assume that the underlying network is known, and then analyze how the graph's algebraic and spectral characteristics impact the properties of the graph signals of interest. However, such assumption is often untenable in practice and arguably most graph construction schemes are largely informal, distinctly lacking an element of validation. The intellectual merit of this research project is to investigate how to use information available from graph signals to learn the underlying graph topology, through innovative approaches that operate in the graph spectral domain. The idea is to consider the graph Fourier transform of the snapshot signals associated with an arbitrary graph and, among all the feasible networks, search for one that endows the resulting transforms with target spectral properties and the sought graph with appealing physical characteristics. Aligned with current trends in data-driven scientific inquiry into complex networked systems, the aim is to shift from: (i) descriptive accounts to inferential graph signal processing techniques that can explain as well as predict network behavior; and from (ii) ad hoc graph constructions to rigorous formulations rooted in well-defined models and principles.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.

应对网络科学和大数据交叉点上的挑战，需要在分布式网络过程的建模、识别和可控性方面取得根本性突破--分布式网络过程通常被概念化为在图形上定义的信号。在对定义在规则域(时间或空间)上的信号的科学理解与图形化信号之间存在明显的不匹配。有关时间序列的知识是在几十年的过程中发展起来的，并受到通信、语音或控制等领域的实际需求的推动。相反，与网络有关的信号处理问题的盛行和高质量网络数据的获取是最近发生的事件。在这种背景下，该项目的研究旨在推动网络分析信息处理方面的知识前沿，从而在理解大脑等强耦合系统的内在复杂性方面取得进展。学生还将接受培训，以解决大数据和网络科学相交的问题，从而为劳动力发展做出贡献。在假设信号与其所支持的图形的拓扑相关的假设下，图形信号处理的目标是开发有效地利用这种关系结构的算法，并在仅部分观察到这些关系时可以对这些关系做出推断。到目前为止，大多数图形信号处理工作都假设底层网络是已知的，然后分析图形的代数和谱特征如何影响感兴趣的图形信号的属性。然而，这样的假设在实践中往往是站不住脚的，而且可以说，大多数图构造方案基本上是非正式的，明显缺乏验证的元素。这项研究项目的智力价值是研究如何使用从图形信号获得的信息来学习潜在的图形拓扑，通过在图形谱域中操作的创新方法。其思想是考虑与任意图相关的快照信号的图傅里叶变换，并在所有可行的网络中搜索一个赋予所得到的变换具有目标谱特性并且所寻找的图具有吸引人的物理特性的网络。与数据驱动的复杂网络系统科学研究的当前趋势相一致，该奖项的目标是从(I)描述性账户转向能够解释和预测网络行为的推理图形信号处理技术；以及(Ii)特别图形构造转向植根于明确定义的模型和原则的严格公式。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（38）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Online Graph Learning under Smoothness Priors

DOI：
10.23919/eusipco54536.2021.9616123
发表时间：
2021-03
期刊：
2021 29th European Signal Processing Conference (EUSIPCO)
影响因子：
0
作者：
S. S. Saboksayr-S.;G. Mateos;M. Çetin
通讯作者：
S. S. Saboksayr-S.;G. Mateos;M. Çetin

Buildup of speaking skills in an online learning community: a network-analytic exploration

在线学习社区中口语技能的培养：网络分析探索

DOI：
10.1057/s41599-018-0116-6
发表时间：
2018
期刊：
Palgrave Communications
影响因子：
4
作者：
Shafipour, Rasoul;Baten, Raiyan Abdul;Hasan, Md Kamrul;Ghoshal, Gourab;Mateos, Gonzalo;Hoque, Mohammed Ehsan
通讯作者：
Hoque, Mohammed Ehsan

Learning Graph Structure from Convolutional Mixtures