III:Small:Discovering and Characterizing Implicit Links in Graph Data


Social media greatly enable people to participate in online activities. A typical social networking site allows users to specify only positive links to other users such as friendships. Little attention is paid to ``negative links" which are implicit links among social users that indicate distrust, dislike, or antagonism. This project studies fundamental data analytics issues of understanding and identifying negative social network links. This project explores new research endeavors to mine actionable and insightful patterns in computer science and to enable the large-scale study of social media user behaviors in computational social science. The research insights gained through this project should benefit the design of new recommender systems and lead to better design of supervised and unsupervised learning algorithms on networks with both positive and negative links. The study of negative social network links can have impact on industrial IT applications by improving services and user experience. The proposed research will involve graduate and undergraduate students in pursuing their theses or projects. Research topics and findings will be integrated in undergraduate and graduate education. The research discovery made through this project will be shared at this project site.

Networks in social media are large-scale, incomplete, and noisy. As negative links between users are often invisible on social networking sites, discovering them entails novel challenges. The research team proposes to evaluate the value of negative links for better social network understanding and link discovery. They propose a group of data analytics techniques, including positive and unlabeled learning, learning with cross-media data, to efficiently predict negative links. The team also proposes to apply the research insights for the better design of recommendation systems, social user classification, and social user clustering. The research team plans to share benchmark data with the research community to promote the research on negative link discovery on social networks.


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    This project is suported by National Science Foundation (NSF) under Grant #1614576 . Any opinions, findings, and conclusions or recommendations expressed here are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

    Created by Huan Liu who can be reached at huan.liu at asu.edu.
    Webmaster: Jundong Li, Email: jundongl at asu.edu

    Last Upadted: Feb 20th, 2018