• The source codes for our implementation are available at http://github.com/iamprith/Ricci-Flow-on-Hypergraphs

• The data for the metabolic networks are available freely from the repository BiGG Models (see the referring paper Bigg models: A platform for integrating, standardizing and sharing genome-scale models for further details)

• For co-authorship networks, for the CSP dataset the three papers selected are:

  1. S. Goldwasser and S. Micali, Probabilistic encryption, Journal of Computer and System Sciences 28, 270 (1984)
  2. R. M. Karp, Reducibility among combinatorial problems, in Complexity of Computer Computations: Proceedings of a symposium on the Complexity of Computer Computations, edited by R. E. Miller, J. W. Thatcher, and J. D. Bohlinger (Springer US, Boston, MA, 1972) pp. 85–103
  3. N. Nisan and A. Wigderson, Hardness vs randomness, Journal of Computer and System Sciences 49, 149 (1994)

  and, for the NSP dataset the three papers selected are:

  1. A.-L. Barabasi and R. Albert, Emergence of scaling in random networks, Science 286, 509 (1999)
  2. M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proceedings of the National Academy of Sciences 99, 7821 (2002)
  3. D.Watts and S. Strogatz, Collective dynamics of ‘smallworld’ networks, Nature 393, 440 (1998)

  For each dataset the following procedure was used. We had selected a specific number of the most cited papers that cite papers 1-3 and papers A-C (900 for papers 1-3, 582 for papers A-C). Then, we built a undirected hypergraph out of these papers and found the biggest connected component.
  • Click here to download the csv file for the biggest connected component for the CSP dataset
  • Click here to download the csv file for the biggest connected component for the NSP dataset
  The above csv files have a column for the paper name, authors, citation count etc.

Copyright (C) 2025 by Prithviraj Sengupta, Nazanin Azarhooshang, Réka Albert and Bhaskar DasGupta. These programs and data are free software and data; you can redistribute it "with proper acknowledgement" under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. These programs are distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.