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BIGDATA: Collaborative Research: IA: Novel Bootstrap Procedures for Efficient Large Social Network Analysis

BIGDATA：协作研究：IA：用于高效大型社交网络分析的新颖引导程序

基本信息

批准号：
1633355
负责人：
Vyacheslav Lyubchich
金额：
$ 8.95万
依托单位：
University of Maryland Center for Environmental Sciences
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633355&HistoricalAwards=false
关键词：
BIGDATA Collaborative Research IA Novel

项目摘要

Understanding the structure and dynamics of social networks is crucial for detecting any anomalous behavior and for managing its impacts. Most existing approaches view a network as a series of snapshots, where a snapshot represents the state of a network in a given time period. Therefore, different network operations need to be individually performed over each snapshot. In reality, online social networks are continuously evolving and therefore, network operations should be automatically performed as networks evolve and need to be done efficiently and reliably. Viewing the problem from this perspective allows us to create a solution that supports advanced, real-world use cases such as tracking the neighborhood of a given node or tracking how network connections evolve in time to determine effective marketing campaigns. These examples indicate the need for efficient computing techniques for important network statistics as the large networks evolve over time. To address this problem, the researchers in this project complement existing distributed evolving social graph analysis techniques with bootstrap and other statistical re-sampling based approaches. The ultimate goal is to develop novel data-driven tools so that when needed, not only certain estimates of statistical network models could be computed efficiently but their estimation errors are reliably quantified. This project primarily targets development of new efficient and robust methods for anomaly and outlier detection on large sparse networks. The resulting methodology provides the following functions: 1) a computationally efficient finite sample inference for an extensive range of network topology statistics; 2) a flexible data-driven characterization of network structure and dynamics, and 3) comprehensively quantifying uncertainty in modeling and estimation of large networks, without imposing restrictive conditions on network model specification. The expected advances are both in research methods - new approaches to data-driven nonparametric inference for large sparse networks and in substantial enhancement of knowledge of network dynamics and formation in the era of digital communication. The project can significantly benefit students by providing a broad exposure to interdisciplinary applications of large network and fostering awareness of interdisciplinary relationships -- hence enhancing their capacity for critical thinking and opening up new career paths.

了解社会网络的结构和动态对于检测任何异常行为和管理其影响至关重要。大多数现有方法将网络视为一系列快照，其中快照表示给定时间段内网络的状态。因此，需要对每个快照分别执行不同的网络操作。在现实中，在线社交网络是不断发展的，因此，网络运营应该随着网络的发展而自动进行，并且需要高效可靠地完成。从这个角度来看问题，可以让我们创建一个解决方案，支持高级的、真实的用例，比如跟踪给定节点的邻域，或者跟踪网络连接如何及时发展，以确定有效的营销活动。这些例子表明，随着大型网络随着时间的推移而发展，需要高效的计算技术来处理重要的网络统计数据。为了解决这个问题，这个项目的研究人员用bootstrap和其他基于统计重新抽样的方法补充了现有的分布式进化社会图分析技术。最终目标是开发新的数据驱动工具，以便在需要时，不仅可以有效地计算统计网络模型的某些估计，而且可以可靠地量化其估计误差。这个项目的主要目标是开发新的高效和鲁棒的方法来检测大型稀疏网络上的异常和离群值。由此产生的方法提供了以下功能：1)计算效率高的有限样本推断广泛的网络拓扑统计；2)灵活的数据驱动网络结构和动态表征；3)在不限制网络模型规范的情况下，对大型网络建模和估计中的不确定性进行全面量化。预期的进展包括研究方法-大型稀疏网络的数据驱动非参数推理的新方法，以及数字通信时代网络动力学和形成知识的实质性增强。该项目可以为学生提供广泛接触大型网络跨学科应用的机会，并培养跨学科关系的意识，从而提高他们的批判性思维能力，开辟新的职业道路，从而使学生受益匪浅。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Using isotope composition and other node attributes to predict edges in fish trophic networks

使用同位素组成和其他节点属性来预测鱼类营养网络中的边缘

DOI：
10.1016/j.spl.2018.06.001
发表时间：
2018
期刊：
Statistics & Probability Letters
影响因子：
0.8
作者：
Lyubchich, Vyacheslav;Woodland, Ryan J.
通讯作者：
Woodland, Ryan J.

GraphBoot: Quantifying Uncertainty in Node Feature Learning on Large Networks

GraphBoot：量化大型网络上节点特征学习的不确定性

DOI：
10.1109/tkde.2019.2925355
发表时间：
2019
期刊：
IEEE Transactions on Knowledge and Data Engineering
影响因子：
8.9
作者：
Akcora, Cuneyt;Gel, Yulia;Kantarcioglu, Murat;Lyubchich, Vyacheslav;Thuraisingam, Bhavani
通讯作者：
Thuraisingam, Bhavani

Topological clustering of multilayer networks