Structify-Net: Random Graph generation with controlled size and customized structureuse asterix (*) to get italics

Remy Cazabet, Salvatore Citraro, Giulio RossettiPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2023

<p>Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size -- number of nodes, edges -- and a customizable structure, beyond blocks and spatial ones, based on node-pair rank and a tunable probability function allowing to control the amount of randomness. We introduce a structure zoo -- a collection of original network structures -- and conduct experiments on the small-world properties of networks generated by those structures. Finally, we introduce an implementation as a Python library named Structify-net.</p>

https://github.com/Yquetzal/structify_netYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

https://github.com/Yquetzal/structify_netYou should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

Network Generation, Random Graphs, Network Structure, Python Library

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Algorithms for Network Analysis, Clustering in networks, Community structure in networks, Geometry and topology of networks or graphs, Graph models, Network models, Random graphs, Spatial networks, Structural network properties

Alec Kirkley suggested: George Cantwell (gcant@umich.edu), Alec Kirkley suggested: Tiago Peixoto (tiago@skewed.de), Alec Kirkley suggested: Jean-Gabriel Young (jean.gabriel.young@gmail.com)

e.g. John Doe john@doe.com

No need for them to be recommenders of PCI Network Sci. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

2023-06-09 10:41:32

Leto Peel

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