Network Science

Structify-Net: Random Graph generation with controlled size and customized structure

10.24072/pcjournal.335 - Peer Community Journal, Volume 3 (2023), article no. e103.

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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.

Published online:
DOI: 10.24072/pcjournal.335
Keywords: Network Generation, Random Graphs, Network Structure, Python Library
Cazabet, Remy 1; Citraro, Salvatore 2; Rossetti, Giulio 2

1 Univ Lyon, UCBL, CNRS, INSA Lyon, LIRIS, UMR5205, F-69622 Villeurbanne, France
2 Institute of Information Science and Technologies “A. Faedo” (ISTI), National Research Council (CNR), Italy
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights
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Cazabet, Remy; Citraro, Salvatore; Rossetti, Giulio. Structify-Net: Random Graph generation with controlled size and customized structure. Peer Community Journal, Volume 3 (2023), article  no. e103. doi : 10.24072/pcjournal.335.

Peer reviewed and recommended by PCI : 10.24072/pci.networksci.100114

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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