Network Science of Teams: Characterization, Prediction, and Optimization

kDD 2018 Tutorial

Time: 1:00 PM - 5:00 PM, August 19, 2018

Abstract

Teams are increasingly indispensable to achievements in any organization. Despite the organizations’ 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. Generally speaking, the team performance can be viewed as the composite of its users, the tasks that the team performs and the networks that the team is embedded in or operates on. The goal of this tutorial is to (1) provide a comprehensive review of the recent advances in optimizing teams’ performance in the context of networks; and (2) identify the open challenges and future trends. We believe this is an emerging and high-impact topic in computational social science, which will attract both researchers and practitioners in the data mining as well as social science research communities. Our emphasis will be on (1) the recent emerging techniques on addressing team performance optimization problem; and (2) the open challenges/future trends, with a careful balance between the theories, algorithms and applications.

Outline (Tentative)

  1. Introduction, why teams and research questions related to teams

  2. Part I: Team Performance Characterization

    1. Collective intelligence

    2. Virtual teams in online games

    3. Networks in sports teams

    4. Networks in Github teams

  3. Part II: Team Performance Prediction

    1. Citation count prediction

    2. Mechanistic model for scientific impact

    3. Long-term performance

    4. Performance trajectory

    5. Joint modeling of parts and whole

  4. Part III: Team Performance Optimization

    1. Team formation and its variants

    2. Team member replacement

    3. Team enhancement

  5. Part IV: Open Challenges

    1. Prediction explanation

    2. Optimization explanation

    3. Multiple teams optimization

Slides and Related Resources

  1. Full Slides: [PDF]

  2. Project Website: Teamwork in Big Networks

Previous Offerings

Presenters

Liangyue Li is a Ph.D. student at School of Computing, Informatics and Decision Systems Engineering at Arizona State University. He received the B.Eng. degree in Computer Science from Tongji University in 2011. His current research interests include large scale data mining and machine learning, especially for large graph data with application to social network analysis. He has published over 10 referred articles in top conferences and journals. He has served as a program committee member in top data mining and artificial intelligence venues (e.g., KDD, SDM, AAAI, CIKM, etc). He has given a keynote talk at CIKM 2016 Workshop on Big Network Analytics (BigNet 2016).

Hanghang Tong is currently an assistant professor at School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Before that, he was an assistant professor at Computer Science Department, City College, City University of New York, a research staff member at IBM T.J. Watson Research Center and a Post-doctoral fellow in Carnegie Mellon University. He received his M.Sc and Ph.D. degree from Carnegie Mellon University in 2008 and 2009, both majored in Machine Learning. His research interest is in large-scale data mining for graphs and multimedia. He has received several awards, including NSF CAREER award (2017), a 10-year highest impact paper award in ICDM 2015, four best paper awards, five ‘bests of conference’, one best demo, honorable mention. He has published over 100 referred articles. He is an associated editor of SIGKDD Explorations (ACM), an action editor of Data Mining and Knowledge Discovery (Springer), and an associate editor of Neurocomping Journal (Elsevier); and has served as a program committee member in multiple data mining, databases and artificial intelligence venues (e.g., SIGKDD, SIGMOD, AAAI, WWW, CIKM, etc). He has given several well-attended tutorials at IEEE Big Data 2015, SDM 2016, ICDM 2013, SDM 2013, ICDE 2009 and CIKM 2008.