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- W4380136705 abstract "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." @default.
- W4380136705 created "2023-06-10" @default.
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- W4380136705 date "2023-06-08" @default.
- W4380136705 modified "2023-10-04" @default.
- W4380136705 title "Structify-Net: Random Graph generation with controlled size and customized structure" @default.
- W4380136705 doi "https://doi.org/10.48550/arxiv.2306.05274" @default.
- W4380136705 hasPublicationYear "2023" @default.
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