CAREER: Developing Evaluation Methods for Network-based Findings

职业：开发基于网络的发现的评估方法

• 批准号: 1942929
• 负责人: Reza Zafarani
• 金额: $ 50万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942929&HistoricalAwards=false
• 关键词: CAREER; Developing; Evaluation; Methods; Network

Networks are all around us: transportation networks, power networks, communication networks, social networks, biological networks, the Internet, and many others. Networks are studied by various disciplines within science and engineering to glean the invaluable insights that networks can carry. However, most studies of networks look at networks that are uncertain, e.g., are noisy. Generalizing findings derived from such uncertain networks can lead to various erroneous findings. The goal of this project is to develop methods that can evaluate findings derived in studies of networks under these uncertainties. The developed techniques contribute towards a future in network science research, where findings are validated, reproducible, and comparable. The outcomes of this project will contribute to the development of a diverse globally-competitive STEM workforce, full participation of women and underrepresented minorities, enhanced research and education infrastructure, and increased public scientific literacy and engagement. The education plan involves four groups: K-12, undergraduate students, graduate students, and beyond university/public. Educational objectives are designed for these groups by integrating research into education, curricular development, presenting tutorials and seminars, and development of software and public data repositories.The project is motivated by the challenges and the research questions that exist around evaluating findings in studies of networks, e.g., whether the findings are specific to the network study, or if the data was correctly collected for the study. Addressing these challenges and answering these questions, the project will lead to the development of new knowledge on evaluating findings derived from networks, advancing the state of research on reproducibility in network science. It will provide a multiangle perspective towards evaluation of network science findings by developing evaluation methods and metrics to assess findings in terms of general scientific evaluation metrics, e.g., authenticity of findings. New network embedding methods will be designed especially for evaluation purposes. In particular, the project will develop specificity metrics for network studies to determine how specific are the findings to a network study. These metrics will be introduced by designing "graph identification" mechanisms, which rely on network embedding methods and designing an ``identity" for a graph. The project will further develop methods that can provide acceptance/rejection likelihoods for network-based findings, as a way to assess their authenticity. While the problems are in general NP-Complete, alternative practical solutions with acceptable performances are pursued by designing "network-based authentication" mechanisms that are inspired by biometrics research. The methods introduced contribute to the broader research areas of network representation and embedding, facilitate new applications such as network identification and authentication, and extend well-known techniques from statistics to networks.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.

网络都在我们周围：运输网络，电力网络，通信网络，社交网络，生物网络，互联网等。科学和工程学中的各个学科对网络进行了研究，以收集网络可以携带的宝贵见解。 但是，大多数网络研究都查看不确定的网络，例如嘈杂。 从这种不确定的网络中得出的发现可以导致各种错误的发现。该项目的目的是开发可以评估这些不确定性下网络研究中得出的发现的方法。开发的技术有助于网络科学研究的未来，该研究结果得到了验证，可再现和可比性。该项目的结果将有助于发展全球多样的企业家劳动力，妇女的充分参与和代表性不足的少数民族，增强了研究和教育基础设施，并提高了公共科学素养和参与度。该教育计划涉及四个小组：K-12，本科生，研究生以及大学/公共之外。教育目标是通过将研究，课程发展，介绍教程和研讨会以及软件和公共数据存储库的开发整合到这些群体中设计的。该项目是出于挑战和研究问题的激励，以及在网络研究中评估发现的研究问题，例如，研究结果是否特定于网络研究，或者是否正确地进行了数据掌握的研究。在解决这些挑战并回答这些问题时，该项目将导致开发有关评估网络中发现的发现的新知识，从而推进了网络科学中可重复性的研究状态。 它将通过开发评估方法和指标来评估网络科学发现的多个观点，以评估一般科学评估指标，例如发现的真实性。新的网络嵌入方法将特别用于评估目的。特别是，该项目将开发网络研究的特异性指标，以确定网络研究的发现的特定程度。 These metrics will be introduced by designing "graph identification" mechanisms, which rely on network embedding methods and designing an ``identity" for a graph. The project will further develop methods that can provide acceptance/rejection likelihoods for network-based findings, as a way to assess their authenticity. While the problems are in general NP-Complete, alternative practical solutions with acceptable performances are pursued by designing "network-based authentication"受生物识别研究启发的机制。引入的方法为网络表示和嵌入的更广泛的研究领域有助于诸如网络识别和身份验证等新应用，并将众所周知的技术从统计学扩展到网络。该奖项反映了NSF的法定任务，并通过评估了该智力的智力。

项目成果

Graph sparsification with graph convolutional networks

• DOI: 10.1007/s41060-021-00288-8
• 发表时间: 2021-10
• 期刊: International Journal of Data Science and Analytics
• 影响因子: 2.4
• 作者: Jiayu Li;Tianyun Zhang;Hao Tian;Shengmin Jin;M. Fardad;R. Zafarani

Graph-Based Identification and Authentication: A Stochastic Kronecker Approach

基于图的识别和认证：随机克罗内克方法

• DOI: 10.1109/tkde.2020.3025989
• 发表时间: 2022
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Jin, Shengmin;Phoha, Vir V.;Zafarani, Reza
• 通讯作者: Zafarani, Reza

“This is Fake! Shared it by Mistake”:Assessing the Intent of Fake News Spreaders

• DOI: 10.1145/3485447.3512264
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM Web Conference 2022
• 作者: Xinyi Zhou;Kai Shu;V. Phoha;Huan Liu;R. Zafarani

AdverSparse: An Adversarial Attack Framework for Deep Spatial-Temporal Graph Neural Networks

• DOI: 10.1109/icassp43922.2022.9747850
• 发表时间: 2022-05
• 期刊: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Jiayu Li;Tianyun Zhang;Shengmin Jin;M. Fardad;R. Zafarani

A Spectral Representation of Networks: The Path of Subgraphs

• DOI: 10.1145/3534678.3539433
• 发表时间: 2022-08
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Shengmin Jin;Hao Tian;Jiayu Li;R. Zafarani