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Visualization of Research Community Networks

Published: 10 September 2023 Publication History

Abstract

The visualization of year-to-year and aggregated collaboration networks can provide valuable insights, enhanced through the exploration of different layouts, to gain more comprehensive understanding of research relationships. They can be employed for the visual assessment of, for example, the impact of HPC centers on research productivity. Overall, the visualization of university networks is an important tool for enhancing understanding of the research landscape and promoting collaboration and innovation in the academic community. Furthermore, it can provide a visually appealing means to showcase research collaborations at the university to external stakeholders.

References

[1]
Gabor Csardi and Tamas Nepusz. 2006. The igraph software package for complex network research. InterJournal Complex Systems (2006), 1695. https://igraph.org
[2]
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. 2008. Exploring Network Structure, Dynamics, and Function using NetworkX. In Proceedings of the 7th Python in Science Conference, Gaël Varoquaux, Travis Vaught, and Jarrod Millman (Eds.). Pasadena, CA USA, 11–15.
[3]
Iqbal Hossain, Stephen Kobourov, and Nirav Merchant. 2021-. Institutional Knowledge Map (KMAP). https://kmap.arizona.edu Accessed 2022-.
[4]
Mathieu Jacomy, Tommaso Venturini, Sebastien Heymann, and Mathieu Bastian. 2014. ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software. PloS one 9, 6 (2014), e98679.
[5]
Jacomyal. 2023. σ sigma.js. https://github.com/jacomyal/sigma.js https://github.com/jacomyal/sigma.js.
[6]
Douglas M. Jennewein, Johnathan Lee, Chris Kurtz, Will Dizon, Ian Shaeffer, Alan Chapman, Alejandro Chiquete, Josh Burks, Amber Carlson, Natalie Mason, Arhat Kobwala, Thirugnanam Jagadeesan, Praful Barghav, Torey Battelle, Rebecca Belshe, Debra McCaffrey, Marisa Brazil, Chaitanya Inumella, Kirby Kuznia, Jade Buzinski, Sean Dudley, Dhruvil Shah, Gil Speyer, and Jason Yalim. 2023. The Sol Supercomputer at Arizona State University. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’23). Association for Computing Machinery, New York, NY, USA, 5 pages. https://doi.org/10.1145/3569951.3597573
[7]
[7] Digital Science. 2018-. https://app.dimensions.ai/Accessed 2021-, under license agreement.
[8]
Olga Scrivner, Gagandeep Singh, Sara E. Bouchard, Scott C. Hutcheson, Ben Fulton, Matthew R. Link, and Katy Börner. 2018. XD Metrics on Demand Value Analytics: Visualizing the Impact of Internal Information Technology Investments on External Funding, Publications, and Collaboration Networks. Frontiers in Research Metrics and Analytics 2 (2018). https://doi.org/10.3389/frma.2017.00010
[9]
Dhruvil D. Shah, Rebecca Belshe, Kirby Kuznia, Gil Speyer, and Jason Yalim. 2023. Employing a Research Community Network to Assess Centralized Computing Impact. In Practice and Experience in Advanced Research Computing (Portland, OR, USA) (PEARC ’23). Association for Computing Machinery, New York, NY, USA, 12 pages. (in press).
[10]
Guido Van Rossum and Fred L. Drake. 2009. Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.

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  • (2024)Measuring the Impact of Centralized High-Performance Computing with Research Collaboration NetworksSN Computer Science10.1007/s42979-024-02890-65:5Online publication date: 29-May-2024

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cover image ACM Conferences
PEARC '23: Practice and Experience in Advanced Research Computing 2023: Computing for the Common Good
July 2023
519 pages
ISBN:9781450399852
DOI:10.1145/3569951
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 10 September 2023

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Author Tags

  1. Network Analysis
  2. Return on Investment
  3. Visualization

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Overall Acceptance Rate 133 of 202 submissions, 66%

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  • (2024)Measuring the Impact of Centralized High-Performance Computing with Research Collaboration NetworksSN Computer Science10.1007/s42979-024-02890-65:5Online publication date: 29-May-2024

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