EAGER: Collaborative Research:Blockchain Graphs as Testbeds of Power Grid Resilience and Functionality Metrics

EAGER：协作研究：区块链图作为电网弹性和功能指标的测试平台

• 批准号: 2039701
• 负责人: Jie Zhang
• 金额: $ 15万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-11-15 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039701&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Blockchain; Graphs

Rapid adoption of renewable energy, growth of distributed generation technologies and microgrid deployment, widespread implementations of IoT devices and associated cybersecurity concerns have led to an ever expanding risk landscape and have, in turn, shifted the focus from energy system protection to resilience. Furthermore, the past two decades have seen a consistently increasing interest in the application of tools developed in the interdisciplinary field of complex network analysis to enhance our understanding of functionality, security, and reliability of power systems and critical infrastructures in which power grid networks serve as an integral constituent. Such systems are intrinsically interconnected and interdependent, and as a result, failure of one component due to manmade and natural cause, e.g., adverse weather, aging, and intentional attacks such as terrorism, can lead to a catastrophic cascade of failures. However, one of the key obstacles for developing systematic and reliable assessment of power grid and critical infrastructure resiliency under various threats is unavailability of ground truth data on system response to adversaries due to data security, privacy and confidentiality. Motivated by these problems rooted in the analysis of modern power grids but inherent to many other complex network systems and given public availability of all blockchain transaction transactions, we propose to use blockchain graphs as a test-bed for validation of functionality and resilience metrics on complex power grid networks.Exploring power grid sensitivity to targeted attacks and failures in scenarios allowing for validation of any proposed methodology against ground truth is the key toward developing reliable risk mitigation strategies for power systems. The novel research approaches to be developed in this project aim to lay foundations for deeper knowledge integration and transfer across power systems, finance, social sciences and other disciplines by utilizing graph-theoretic analysis and adopting a common mathematical language of computational topology. By promoting new synergistic research initiatives, the project will further advance knowledge creation not only on power grids and blockchain but on modern complex networks, from brain connectome to social media platforms to telecommunication-- thereby facilitating resilience and sustainability of our society.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.

可再生能源的快速采用、分布式发电技术和微电网部署的增长、物联网设备的广泛实施以及相关的网络安全问题，导致了风险环境的不断扩大，并反过来将重点从能源系统保护转移到弹性上。此外，在过去的二十年里，人们对复杂网络分析跨学科领域开发的工具的应用越来越感兴趣，以增强我们对电力系统和关键基础设施的功能、安全性和可靠性的理解，其中电网网络是不可或缺的组成部分。这些系统本质上是相互联系和相互依赖的，因此，由于人为和自然原因（例如恶劣天气、老化和恐怖主义等故意攻击）导致的一个组件的故障可能导致灾难性的故障级联。然而，在各种威胁下对电网和关键基础设施弹性进行系统可靠评估的关键障碍之一是，由于数据安全、隐私和机密性，无法获得系统对对手响应的地面真实数据。这些问题植根于对现代电网的分析，但也存在于许多其他复杂的网络系统中，并且考虑到所有区块链交易的公共可用性，我们建议使用区块链图作为验证复杂电网网络功能和弹性指标的测试平台。探索电网对有针对性的攻击和故障的敏感性，以便根据实际情况验证任何提议的方法，这是为电力系统制定可靠的风险缓解策略的关键。本项目将开发新的研究方法，旨在通过利用图论分析和采用通用的计算拓扑数学语言，为电力系统、金融、社会科学和其他学科之间更深层次的知识整合和转移奠定基础。通过促进新的协同研究举措，该项目将进一步推进知识创造，不仅涉及电网和区块链，还涉及从大脑连接体到社交媒体平台再到电信等现代复杂网络，从而促进我们社会的复原力和可持续性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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

Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT

• DOI: 10.48550/arxiv.2211.13708
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: C. Akcora;Murat Kantarcioglu;Y. Gel;Baris Coskunuzer

Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains

• 发表时间: 2022
• 作者: Kiarash Shamsi;Friedhelm Victor;Murat Kantarcioglu;Y. Gel;C. Akcora

Tlife-GDN: Detecting and Forecasting Spatio-Temporal Anomalies via Persistent Homology and Geometric Deep Learning

Tlife-GDN：通过持久同源性和几何深度学习检测和预测时空异常

• 发表时间: 2022
• 期刊: Pacific-Asia Conference on Knowledge Discovery and Data Mining
• 作者: Zhen, Zhiwei;Chen, Yuzhou;Segovia-Dominguez, Ignacio;Gel, Yulia R.
• 通讯作者: Gel, Yulia R.

Topological Relational Learning on Graphs

• 发表时间: 2021-10
• 作者: Yuzhou Chen;Baris Coskunuzer;Y. Gel

BScNets: Block Simplicial Complex Neural Networks

• DOI: 10.1609/aaai.v36i6.20583
• 发表时间: 2021-12
• 作者: Yuzhou Chen;Y. Gel;H. Poor