CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links

职业：现实世界网络：具有正向和负向链接的签名网络的建模和分析

• 批准号: 1845081
• 负责人: Jiliang Tang
• 金额: $ 50.77万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845081&HistoricalAwards=false
• 关键词: CAREER; Real; World; Networks; Modeling

In many real-world social systems, in addition to positive links, relations between people can be negative (e.g., foes, blocked and unfriended users, and distrust). These relations can be represented as networks with both positive and negative links (or signed networks). Signed networks have substantially different properties and principles from unsigned ones, which poses tremendous challenges to traditional network analysis and requires dedicated efforts. Thus a systematic and comprehensive investigation on signed networks is desired. The results of this project can have an immediate and strong impact on improving the performance of various network analytical tasks, enabling the analysis of networks with negative links, and thus positively impacting the overall value of various data/information areas. The developed new algorithms for signed network analysis will have impact on various disciplines, including computer science, social science, health informatics, and education as signed networks are very common in these domains. This project will play an integral part to attract undergraduate and K-12 students especially underrepresented groups to careers in engineering, to inform them about crucial but highly unavailable network analysis technologies and to encourage and train computer science and engineering graduate and undergraduate students to address research issues in network analysis.The added complexity of negative links in signed networks has manifested unprecedented research challenges and opportunities. This project will comprehensively investigate the primary directions of signed networks from modeling and measuring to mining. Each direction will dramatically extend the frontier through not only developing innovative solutions, but also studying original problems. The core intellectual merit lies in the fact that the project offers the first systematic investigation on this emerging research area and the designed advanced methodologies and novel tasks will deepen our understanding on how negative links can be synergized to advance the field of network analysis; improve our knowledge of real-world networks; and contribute to real-world applications.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.

在许多现实世界的社会系统中，除了积极的联系之外，人与人之间的关系也可能是消极的（例如，敌人、被屏蔽和未加好友的用户以及不信任）。这些关系可以表示为具有正链接和负链接的网络（或带符号网络）。 有符号网络与无符号网络具有本质上不同的性质和原理，这对传统的网络分析提出了巨大的挑战，需要专门的工作。因此，需要对签名网络进行系统而全面的研究。这一项目的成果可以对改善各种网络分析任务的性能产生直接和强烈的影响，从而能够分析具有负面联系的网络，从而对各种数据/信息领域的整体价值产生积极影响。符号网络分析的新算法将对各个学科产生影响，包括计算机科学，社会科学，健康信息学和教育，因为符号网络在这些领域非常常见。该项目将发挥不可或缺的一部分，以吸引本科生和K-12学生，特别是代表性不足的群体在工程职业，让他们了解关键但高度不可用的网络分析技术，并鼓励和培训计算机科学与工程研究生和本科生解决网络分析中的研究问题。和机会。该项目将全面研究签名网络从建模和测量到挖掘的主要方向。 每个方向不仅通过开发创新解决方案，还通过研究原创问题，极大地扩展了前沿领域。 该项目的核心智力价值在于，该项目首次对这一新兴研究领域进行了系统研究，设计的先进方法和新颖任务将加深我们对如何协同负面联系以推进网络分析领域的理解;提高我们对现实世界网络的认识;该奖项反映了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。