Network Science Institute

rom this nexus of cross-domain exchanges, our team has a strong track record developing unconventional strategies to help solve some of the most complex problems that range from biological, social and geospatial systems, to cyber, wireless, and technological challenges. Research conducted in the institute covers fundamental mathematical and theoretical discoveries about networks, including geometry of networks, representation and manipulation of multi-dimensional networks. While domain-specific inquiries have provided novel insights on brain functions, disease processes, disease spread through populations, predictions of emerging scientific innovations and breakthroughs, group learning and consensus, and political movements. The Institute is comprised of three foundational components:

Network Science enables exploration of complex networked systems using a common platform, rooted in the insight that despite their diversity of behavior, the dynamics of different kinds of networks are characterized by many common organizing principles. Dramatic improvements in information technology over the past 20 years, including increased storage capacity and computing power, has made it possible to archive and study massive amounts of data. Large-scale datasets describing topics ranging from mobile phone usage, to medical records, to biological signaling pathways and climate change are being built and curated by myriad organizations throughout the world. The emerging field of network science, in which complex systems of interconnected components are studied, presents an opportunity to leverage these technology-based advances for the benefit of both science and society. Network Science enables exploration of complex networked systems using a common platform, rooted in the insight that despite their diversity of behavior, the dynamics of different kinds of networks are characterized by many common organizing principles and can thus be studied with similar tools using methodological advances in computational thinking, complex systems analysis, and data integration. The ambition to understand the behaviors of complex systems, and to apply these findings to problems in public health, social science, security, and biomedical sciences, among other domains, is a significant undertaking that is gaining momentum in academic, government and corporate spheres. The challenge ahead is to develop theories, tools, methodologies and datasets that are flexible and scalable so that they can be applied across scientific domains in meaningful and productive ways.