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

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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.