CINET - A Cyber-Infrastructure for Network Science

Project Details

Project Lead
Keith Bisset 
Project Manager
Keith Bisset 
Project Members
Hemanth Makkapati  
Virginia Tech, Virginia Bioinformatics Institute/NDSSL  
Computer Science (401) 


CINet is a cyberinfrastructure middleware to support Network Science. The National Research Council defines Network Science as “the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena.” This middleware will give Network Scientists access to an unparalleled computational and analytic environment for research, education and training. There is a growing importance of networks in diverse fields. Recent results in the emerging field Network Science enhance our understanding of physical and social systems. Advances in computing and information systems provide motivation for developing this middleware. Network Science often deals with very large graphs and time-consuming computations on those graphs that require more computing power than is available on the typical desktop. Scientists working with networks are often not expert computer users; this has hampered the adoption of High Performance Computing (HPC)-based modeling methodologies and environments. By harnessing new cloud-based resources, such as Magellan and FutureGrid, in an easily accessible manner, the proposed work will enable Network Science researchers to tackle larger, more complex problems. The project vision is to provide researchers, analysts and educators interested in Network Science with an easy-to-use cyber-environment that is accessible from their desktop and integrates into their daily work. A key goal is to greatly expand the size of networks that are routinely studied from hundreds or thousands of nodes to hundreds of millions of nodes. It will leverage the technology, data and experience of a multi-institutional team (Virginia Tech, Indiana University, University of Houston-Downtown, University of Chicago and Argonne National Laboratory) in this area. The idea for the project grew out of frequent requests for access to large synthetic populations and associated models generated at Virginia Tech, as well as the difficulty VT researchers faced acquiring access to needed datasets. Please see for more information.

Intellectual Merit

CINet will enable fundamental changes in the way researchers study and teach complex networks. The use of state-of-the-art computing resources to synthesize, analyze, store and reason about large networks will enable researchers and educators to study networks in novel ways. It will enable educators to harness HPC technologies to teach Network Science to students spanning various academic levels, disciplines and institutions. It will be designed for scalability, usability, extensibility and continuity. The investigators also will advance the fields of digital libraries and grid computing by stretching them to address challenges related to Network Science.

Broader Impacts

The investigators will launch a comprehensive education and outreach plan comprised of short courses, workshops at important conferences and focused user group meetings to provide a path towards adoption of the cyberinfrastructure and suggest user-guided improvements. The educational plan includes high school students to Ph.D. candidates, students from minority and under-represented groups, and students at smaller institutions that often do not have easy access to HPC resources. Network Science is a transdisciplinary topic; the proposed platform will foster multi-disciplinary and multi-university research and teaching collaborations, including areas which have not traditionally made use of HPC resources such as the social sciences.

Scale of Use

FutureGrid will be one of the compute resources available for use by CINet. One small VM will need to be online continuously to run the local resource broker. This broker will start and stop VMs in response to the compute requests that are sent to FutureGrid. The central resource broker, run on Virginia Tech resources, can be tuned to send an appropriate number of compute requests to FG so as to achieve the desired utilization.