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

在许多现实社会系统中，除了积极的联系外，人与人之间的关系也可能是消极的(例如，敌人、被屏蔽和取消好友的用户以及不信任)。这些关系可以表示为具有正向链接和负向链接的网络(或带符号的网络)。已签名网络的性质和原理与未签名网络有很大不同，这给传统的网络分析带来了巨大的挑战，需要专门的努力。因此，需要对签名网络进行系统而全面的研究。该项目的结果可以对改进各种网络分析任务的绩效产生直接和强烈的影响，使分析具有负面链接的网络成为可能，从而对各种数据/信息领域的整体价值产生积极影响。新开发的签名网络分析算法将对计算机科学、社会科学、卫生信息学和教育等多个学科产生影响，因为这些领域中签名网络非常常见。这个项目将发挥不可或缺的作用，吸引本科生和K-12年级的学生，特别是代表不足的群体，进入工程领域，向他们介绍关键但高度可用的网络分析技术，并鼓励和培训计算机科学和工程研究生和本科生解决网络分析中的研究问题。已签署的网络中的负链接增加了复杂性，体现了前所未有的研究挑战和机遇。该项目将全面研究符号网络从建模、测量到挖掘的主要方向。每个方向都将极大地扩展前沿，不仅通过开发创新的解决方案，而且还通过研究原始问题。该项目的核心知识价值在于，该项目提供了对这一新兴研究领域的首次系统调查，设计的先进方法和新颖的任务将加深我们对如何协同负面链接以推进网络分析领域的理解；提高我们对现实世界网络的了解；并为现实世界的应用做出贡献。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。

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.