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.

网络无处不在：交通网络、电力网络、通信网络、社会网络、生物网络、互联网等等。科学和工程领域的不同学科都对网络进行研究，以收集网络可以承载的宝贵见解。然而，大多数网络研究着眼于不确定的网络，例如，噪声网络。对这种不确定网络得出的结论进行泛化可能会导致各种错误的发现。这个项目的目标是开发能够评估在这些不确定性下的网络研究中得出的结果的方法。开发的技术有助于网络科学研究的未来，在那里，研究结果是有效的、可重复的和可比较的。该项目的成果将有助于发展一支具有全球竞争力的多样化的STEM劳动力队伍，促进妇女和代表性不足的少数群体的充分参与，加强研究和教育基础设施，并提高公众的科学素养和参与度。教育计划涉及四个群体：K-12、本科生、研究生和大学/公立学校以外的学生。通过将研究融入教育、课程开发、提供教程和研讨会以及开发软件和公共数据库，为这些群体设计教育目标。项目的动机是围绕评估网络研究结果存在的挑战和研究问题，例如，研究结果是否专用于网络研究，或者数据是否为研究正确收集。应对这些挑战并回答这些问题，该项目将有助于形成评价网络研究成果的新知识，推动网络科学可再现性研究的进展。它将为网络科学成果的评价提供一个多角度的视角，方法是制定评价方法和指标，根据一般科学评价指标，如结论的真实性，对成果进行评估。将专门为评估目的设计新的网络嵌入方法。特别是，该项目将制定网络研究的特异性指标，以确定网络研究的结果有多具体。这些指标将通过设计“图识别”机制来引入，该机制依赖于网络嵌入方法和为图设计“身份”。该项目将进一步开发能够为基于网络的调查结果提供接受/拒绝可能性的方法，作为评估其真实性的一种方式。虽然这些问题通常是NP-完全的，但在生物特征识别研究的启发下，通过设计基于网络的认证机制来寻求具有可接受性能的替代实用解决方案。介绍的方法有助于网络表示和嵌入的更广泛的研究领域，促进新的应用，如网络识别和认证，并将众所周知的技术从统计扩展到网络。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

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

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

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