CAREER: Signed Networks: Modeling, Measuring, and Mining


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.



  • Tyler Derr, Zhiwei Wang, Jamell Dacon, and Jiliang Tang. Link and Interaction Polarity Predictions in Signed Networks. Social Network Analysis and Mining. 2020.
  • Amin Javari, Tyler Derr, Pouya Esmalian, Jiliang Tang, and Kevin Chen-Chuan Chang. ROSE: Role-based Signed Network Embedding. In Proceedings of the 29th International Conference on The World Wide Web (WWW), Taipei, Taiwan, April 20-24, 2020.
  • Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu. Traffic Flow Prediction via Spatial Temporal Graph Neural Network. In Proceedings of the 29th International Conference on World Wide Web Companion (WWW), 2020
  • Wentao Wang, Suhang Wang, Wenqi Fan, Zitao Liu, and Jiliang Tang. Global-and-Local Aware Data Generation for the Class Imbalance Problem. In Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), 2020
  • Zhiwei Wang, Hui Liu, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu. Learning Multi-level Dependencies for Robust Word Recognition. In The 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, and Jiliang Tang. Graduate Employment Prediction with Bias. In The 34th AAAI Conference on Artificial Intelligence (AAAI), 2020.
  • Tyler Derr, Yao Ma, Wenqi Fan, Xiarui Liu, Charu Aggarwal, and Jiliang Tang. Epidemic Graph Convolutional Network. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM), 2020.
  • Tyler Derr, Cassidy Johnson, Yi Chang, and Jiliang Tang. Balance in Signed Bipartite Networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2019.
  • Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. Graph Convolutional Networks with EigenPooling. In Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2019
  • Tyler Derr, Hamid Karimi (co-first authors), Aaron Brookhouse, and Jiliang Tang. Multi-Factor Congressional Vote Prediction. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019.
  • Zhiwei Wang, Xiaoqin Feng, Jiliang Tang, Gale Yan Huang and Zitao Liu. Knowledge tracing with side information. In The 20th International Conference on Artificial Intelligence in Education (AIED), 2019.
  • Zhiwei Wang, Xiaorui Liu, Jiliang Tang and Dawei Yin. Weight Loss Prediction in Social-Temporal Context. The Seventh IEEE International Conference on Healthcare Informatics (ICHI), 2019

Related Links

  • Jiliang Tang, Yi Chang, Charu Aggarwal, and Huan Liu. "A Survey of Signed Network Mining in Social Media", ACM Computing Surveys 49(3): 42:1-42:37, 2016

Project Members


This project is suported by National Science Foundation (NSF) under Grant IIS-1845081. 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 Jiliang Tang who can be reached at tangjili at

Last Upadted: April 8th, 2020