Network Science Collaborative Technology Alliance (Network Science CTA)

RESEARCH Now in its tenth and final year, the NS CTA is a vigorous and highly productive research collaboration. The NS CTA Consortium funds 14 universities and 3 industrial research labs; our 11 research tasks involve the contributions of 50 primary researchers, including 30 faculty members, 5 senior industry researchers, and 15 government researchers. We have over 1000 publications in a range of venues including Nature, Science, Physical Review, and Proceedings of the National Academy of Sciences, with over 15,000 citations to date. NS CTA researchers are editors or steering committee members for all three of the refereed journals devoted to network science. The NS CTA research program for Y10 is structured as three interdisciplinary technical areas (or thrusts), each of which unites Alliance expertise in social/cognitive networks, information networks, and communication networks. These areas are Co-evolution and Dynamics of Inter-genre Networks (Co-EDIN); Information Processing Across Networks for Decision-Making (IPAN); and Quality of Information for Semantically-Adaptive Networks (QoI-SAN). There is also an integrated Experimentation effort involving all thrusts. We see a world, less than a decade from now, where the rapidly maturing field of network science allows us to predict and influence the behaviors of inter-genre and interdisciplinary network systems so complex that they are opaque to the best science today. Information about Year 10 (2019) research thrusts: Co-evolution and Dynamics of Inter-genre Networks (Co-EDIN): Foundational science for modeling, understanding, predicting, controlling, and optimally designing co-evolving inter-genre networks, both friendly and adversarial. Information Processing Across Networks for Decision-Making (IPAN): Information discovery, analysis, and presentation over multi-genre networks to improve effectiveness in distributed decision making Quality of Information for Semantically-Adaptive Networks (QoI-SAN): Measure, predict, and adapt composite networks to deliver the most valuable information with dynamically changing network resources, rather than the most bits, or queries. The research is motivated by Army and military needs for better capabilities for understanding, predicting, and influencing networks. Some of these needs are illustrated in a motivating mini-scenario. A list of Year 6 and 7 projects (2015 and 2016) can be viewed here. Year 4 and 5 (2013 and 2014) projects can be viewed here. Year 3 (2012) projects can be viewed here, Year 2 (2011) projects here, and Year 1 (2010) projects here.