Zeros and Ones floating outside of teal blue information fountain
Network Systems Science and Advanced Computing | Resilient Societies and Interdependent Infrastructures

CINES: A CyberInfrastructure for Network Engineering and Science

Sponsor

National Science Foundation

Networks are pervasive. A Google search for “networks” returns about 4.8 billion hits and Google Scholar returns over 5 million entries. Companies such as Facebook, Twitter, Amazon, Yahoo, Google, LinkedIn and Akamai use networks as a central concept in their businesses. More than 20 journals and 15 international conferences are devoted to findings about networks, or have them as a significant theme. Many books have been written on this topic. They span many areas including biology, social sciences, information sciences, health sciences, infrastructure systems, business, economics, communication networks, cybersecurity, mathematics, environmental sciences, and ecology.

The XSEDE science gateway lists more than 40 gateways, but none is devoted to network science in general. The Science Gateways Community Institute (SGCI) has roughly 600 gateway entries. A search of the latter for networks and network science returns over 15 individual gateways for network visualization and analyses for genomics, proteins, metabolics, biology, genes and transcription factors, biochemistry, systems biology, computer networks, environmental and earth sciences, diseases and health informatics, and computational neuroscience. Hence, current gateways do not fill the wide-ranging needs for a general-purpose cyberinfrastructure (CI) for network science.

Project Overview

The major goal of this project is to design, build, verify, and deploy an open-access general-purpose CI for network science, which we call net.science. The CI consists of four broad elements:

  1. web application for user interaction with the system through her web browser;
  2. computational codes that perform operations on graphs such as analyses and visualization;
  3. graphs (networks) that can be directed or undirected, labeled and unlabeled, multigraphs, and graph collections, as well as data that supports graphs; and
  4. software infrastructure that stores, manages, executes, and coordinates computational jobs and data within the system.

While net.science will be largely domain-agnostic, the system can equally support domain-specific applications. Another goal is to attract a strong user base so that content creators (e.g., software developers) will want to contribute computational codes and data to the system—where they will be fully acknowledged and credited for their contributions—and in this way, create a community resource.

Findings

The project started in late 2019. Since that time, we have been developing core capabilities. The system will go live in 2021 with an initial set of capabilities that will be expanded over time.

See the home page for more details.

Tools

The current flagship paper that overviews the system is: Nesreen K. Ahmed, Richard A. Alo, Catherine T. Amelink, Young Yun Baek, Aashish Chaudhary, Kristy Collins, Albert C. Esterline, Edward A. Fox, Geoffrey C. Fox, Aric Hagberg, Ron Kenyon, Chris J. Kuhlman, Jure Leskovec, Dustin Machi, Madhav V. Marathe, Natarajan Meghanathan, Yasuo Miyazaki, Judy Qiu, Naren Ramakrishnan, S. S. Ravi, Ryan A. Rossi, Rok Sosic, Gregor von Laszewski, “net.science: A Cyberinfrastructure for Sustained Innovation in Network Science and Engineering,” Gateways Conference, 2020.

This paper and others can be seen on the “Publications” page of net.science.

Team

Division Director

Distinguished Professor in Biocomplexity, Biocomplexity Institute

Professor of Computer Science, School of Engineering and Applied Science

Research Associate Professor

Senior Software Architect

Research Professor

In the News
Network Science

The University of Virginia’s Biocomplexity Institute was recently awarded a five-year $4 million collaborative grant from the U.S. National Science Foundation (NSF) to build a self-sustaining cyberinfrastructure (CINES – pronounced “science”) to serve as an open-source, web-based repository for developing, trading, analyzing, and sharing network science resources.