Teamwork in Big Networks


Teams are increasingly indispensable to achievement in any organization. Despite our substantial dependency on teams, fundamental knowledge about the conduct of team-enabled operations is lacking, especially at the social, cognitive and information level in relation to team performance and network dynamics.

The emergence of network science and the advent of big data era create a brand new environment where different users collaborate with each other to collectively perform complex cognitive tasks by providing an easier-than-ever access to the massive knowledge base as well as the broad social connectivity at an unprecedented scale and granularity.

The goal of this project is two-fold:

  1. understand the dynamic associational and causal mechanisms that drive peak team performance

  2. create a suite of new instruments to predict, explore and design high-performing teams


Video demo: [Link]

System entry: [Link]

System tutorial: [Slides]

Source code: [Link]


This project is an on-going multi-disciplinary, multi-institute effort, coordinated by the Data Lab @ ASU led by Dr. Hanghang Tong, with contributions from many leading researchers (please refer to the related papers below).

Main Contacts:

  • Mr. Liangyue Li (ASU, for team prediction and design algorithms; and site maintenance)

  • Dr. Nan Cao (NYU, for system architecture, graph visualization and HCI)

Papers & Presentations (Selected)

  • Hanghang Tong. Towards Optimal Teams in Big Networks. Invited Talk at 2nd International Workshop on Machine Learning Methods for Recommender Systems (MLRec), 2016. [Slides]

  • Liangyue Li, Hanghang Tong, Jie Tang, Wei Fan. iPath: Forecasting the Pathway to Impact. SIAM International Conference on Data Mining (SDM), 2016. [PDF][Slides]

  • Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu-Ru Lin, Norbou Buchler. Replacing the Irreplaceable: Fast Algorithm for Team Member Recommendation. International World Wide Web Conference (WWW), 2015. (Acceptance Rate: 14.1%) [PDF][Slides][Code]

  • Nan Cao, Yu-Ru Lin, Liangyue Li, Hanghang Tong. g-Miner: Interactive Visual Group Mining on Multivariate Graphs. ACM Conference on Human Factors in Computing Systems (CHI), 2015. [PDF] [Slides]

  • Liangyue Li, Hanghang Tong. The child is Father of the Man: Foresee the Success at the Early Stage. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015. [PDF][Slides]

  • Liangyue Li, Hanghang Tong, Yanghua Xiao, Wei Fan. Cheetah: Fast Graph Kernel Tracking on Dynamic Graphs. SIAM International Conference on Data Mining (SDM), 2015. [PDF] [Slides][Code]

  • Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu. Predicting long-term impact of CQA posts: a comprehensive viewpoint. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014. [PDF][Slides]

  • Yuan Yao, Hanghang Tong, Xifeng Yan, Feng Xu, Jian Lu. Multi-Aspect \(+\) Transitivity \(+\) Bias: An Integral Trust Inference Model. IEEE Trans. Knowl. Data Eng. 26(7): 1706-1719 (2014). [PDF]

  • Danai Koutra, Hanghang Tong, David Lubensky. BIG-ALIGN: Fast Bipartite Graph Alignment. International Conference on Data Mining (ICDM), 2013. [PDF]

  • Kate Ehrlich and Hanghang Tong. The effect of leader centrality on team performance. WIDS, 2012.

  • U. Kang, Hanghang Tong, Jimeng Sun. Fast Random Walk Graph Kernel. SIAM International Conference on Data Mining (SDM), 2012. [PDF]

  • Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw K. Szymanski. GenDeR: A Generic Diversified Ranking Algorithm. Annual Conference on Neural Information Processing Systems (NIPS), 2012. [PDF]

  • Hanghang Tong, Jingrui He, Zhen Wen, Ravi Konuru, Ching-Yung Lin. Diversified ranking on large graphs: an optimization viewpoint. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011. [PDF][Slides]