CAREER: Developing Evaluation Methods for Network-based Findings

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。科学和工程领域的各个学科都在研究网络，以收集网络可以携带的宝贵见解。然而，大多数网络研究关注的是不确定的网络，例如有噪声的网络。从这种不确定的网络中得出的结论可能会导致各种错误的结论。该项目的目标是开发方法，以评估在这些不确定性下的网络研究中得出的结果。开发的技术有助于网络科学研究的未来，其中的发现是有效的，可重复的，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STEM劳动力队伍，促进妇女和代表性不足的少数民族的充分参与，加强研究和教育基础设施，提高公众科学素养和参与度。该教育计划涉及四个群体：K-12、本科生、研究生和大学/公立大学以外的学生。教育目标是为这些群体设计的，包括将研究融入教育、课程开发、提供教程和研讨会、开发软件和公共数据存储库。该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，这些发现是否特定于网络研究，或者研究数据是否正确收集。解决这些挑战并回答这些问题，该项目将导致评估来自网络的发现的新知识的发展，推进网络科学可重复性的研究状态。它将通过开发评估方法和指标，根据一般科学评估指标（例如，发现的真实性）评估发现，为评估网络科学发现提供多角度的视角。新的网络嵌入方法将专门为评估目的而设计。特别是，该项目将为网络研究制定特异性指标，以确定网络研究结果的特异性程度。这些指标将通过设计“图形识别”机制来引入，该机制依赖于网络嵌入方法并为图形设计“身份”。该项目将进一步开发方法，为基于网络的研究结果提供接受/拒绝的可能性，作为评估其真实性的一种方式。虽然这些问题一般都是np完备的，但通过设计受生物识别研究启发的“基于网络的身份验证”机制，寻求具有可接受性能的替代实际解决方案。所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。

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.