CAREER: Harnessing the Positive Power of Negative Links for Network Analytics

今天的大部分大数据可以最好地表示为网络，这导致了网络分析的兴起，以利用这种固有的结构来最好地捕获数据。然而，大多数传统的网络分析方法主要是在没有考虑建模数据中的负关系的情况下开发的。例如，在线社交媒体用户不仅可以与彼此建立积极的友谊，而且他们还可以取消关注，阻止和发展不信任甚至仇恨。该项目的目标是提供专门的网络分析方法，以充分利用数据中的积极关系和消极关系，这对于更好地分析过去和预测未来都是必要的。该项目的成果将推动最先进的网络分析方法，在包括工业和学术界、在线社交媒体和电子商务在内的多个领域产生强大而直接的影响。该项目将在通过开发的本科和研究生课程向学生介绍现有网络分析方面发挥不可或缺的作用，同时还通过学期项目提供第一手研究经验。此外，该项目还将通过各种大学项目，为代表性不足的群体提供指导，包括第一代和神经多样性学生，以发挥他们的独特优势，促进下一代科学家和工程师的多样性。该项目的重点是推进方法，理论，模型和测量，为负链接网络，称为，签署网络。该项目将致力于对具有负面链接的网络进行更深入的分析和更好的预测，同时跨越网络分析的四个主要支柱。具体而言，提出了新的网络理论，并建立在复杂的正/负链接相互作用的基础上，将开发专用的符号图神经网络，以克服现有模型中存在的挑战，改进符号网络建模，以实现更好的综合生成和评估，并最终实现鲁棒的符号网络测量。该项目的动机是前所未有的机会，利用负链接的附加功能来改善网络分析，同时解决与签名网络上的挖掘和学习相关的研究挑战。每一个被提议的研究方向都定位于通过调查原始的未探索的问题和开发新的理论和方法来推进网络分析。这个奖项反映了NSF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。

Much of today's big data can be best represented as networks, which has led to the rise of network analytics for harnessing this inherent structure to best capture the data. However, most traditional network analytical methods have primarily been developed without consideration of modeling negative relations within the data. For example, online social media users can not only create positive friendships with one another, but they can also unfollow, block, and develop distrust or even animosity towards each other. The goal of this project is to provide dedicated advancements in network analysis methods built to fully harness not only the positive but also these negative relations in data, which is both desired and essential to better analyze the past and make predictions about the future. The results of this project will advance state-of-the-art network analysis methods having both a strong and immediate impact in multiple domains including in industry and academia, online social media and e-commerce. This project will play an integral role in introducing existing network analytics to students through developed undergraduate and graduate courses, while also providing first-hand research experiences through semester projects. Additionally, this project will support the mentorship of underrepresented groups through various university programs, including first-generation and neurodiverse students to leverage their unique strengths and to promote diversity in our next generation of scientists and engineers.This project is focused on the advancement of methods, theories, models, and measurements, for networks with negative links, known as, signed networks. The project will provide dedicated efforts towards deeper analysis and better predictions on networks with negative links while spanning the four main pillars of network analytics. Specifically, novel network theories are proposed and built upon the complex positive/negative link interactions, dedicated signed graph neural networks will be developed for overcoming existing challenges in prior models, improved signed network modeling towards better synthetic generation and evaluation, and finally robust signed network measurements. The project is motivated by unprecedented opportunities to harness the additive power of negative links to improve network analytics, but also in solving the research challenges associated with mining and learning on signed networks. Each of the proposed research directions is positioned to advance network analytics through investigating original unexplored problems and developing novel theories and methodologies.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.