Grants

Multidisciplinary University Research Initiative (MURI) on the Network Science of Teams
(Funded by the Army)

The recent convergence of research in social and psychological sciences, dynamic and quantitative modeling, and network science has led to a re-examination of collective team behavior from a quantitative and systems-oriented viewpoint. Teams cannot be understood fully by studying their components (members) in isolation: team performance is not simply a sum of individual performances; and a diversity of opinions among members leads to better group outcomes. However, it is not yet understood how patterns of interactions and relationships among team members (i.e. team networks) impact performance. Understanding these patterns is critical, as the resolution of complex issues requires deliberative within-group interaction processes in which alternative courses of action are surfaced, evaluated, and acted upon. This project aims to build quantifiable informative models of teams as dynamical systems interacting over multiple networks, analyze dynamic team behavior by developing rigorous models that relate interaction patterns and network evolution to task performance, and break new ground in team design by scaling teams to solve complex tasks (i.e. teams of teams), and advancing social science theories of team performance. Besides UCSB, our team includes leading scientists from MIT, USC, UIUC, and Northwestern.

Interdisciplinary Graduate Education, Research, and Training (IGERT) Program on Network Science
(Funded by the National Science Foundation)

Our IGERT program establishes an interdisciplinary graduate program in Network Science involving faculty and graduate students in seven departments: Computer Science (CS), Communication, Ecology, Evolution & Marine Biology (EEMB), Electrical & Computer Engineering (ECE), Geography, Mechanical Engineering (ME), and Sociology. The emphasis areas include computational methods that advance data-enabled science and engineering (scalable algorithms and Cyberinfrastructure), dynamics and control, social networks, and biological networks. The program prepares students to engineer and control large networks, measure and predict the dynamics of networks, design algorithms to operate at scales of millions and billions of entities, make such networks robust, and develop new scientific hypotheses and principles about networks. Such a training is clearly infeasible by any one discipline. UCSB has the experience in relevant interdisciplinary programs to create a cohort of PhD students who understand Network Science not from a single viewpoint, but from a unified perspective that is essential for continued success and career growth in the increasingly network-centric world. There is growing demand for such a trained interdisciplinary workforce from multiple domains of science, commerce, and national security, intervention strategies in social networks to counter the spread of misinformation, and discovery of clandestine terrorism activity.

Discovering Network Processes in Multilayer Time-evolving Networks under Incomplete Information
(Funded by the Army)

The Network Science Collaborative Technology Alliance (NS CTA) is a collaborative research alliance between the US Army Research Laboratory (ARL), other government researchers, and a consortium of university researchers. Its broad objective is to perform foundational cross-cutting research on network science utilizing social/cognitive networks, information networks, and communication networks. Our specific goal is to model and predict network evolution and the evolution of dynamic processes occurring within these networks utilizing partial observations. Questions we seek to answer include: Can we uncover hidden network layers from observed dynamic processes occurring on time-varying multilayer networks? How and where should we measure such networks? Can we use this to predict the process dynamics and detect anomalies? Can we predict network dynamics and dynamic processes on time-varying networks in the presence of missing temporal and/or structural data? How to model mixture of network processes at different temporal and structural scales? How much of missing data can be tolerated? What is the effect of missing data on a multilayered network?