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Network structure is often considered one of the most important features of anetwork, and various models exist to generate graphs having one of the moststudied types of structures, such as blocks/communities or spatial structures.In this article, we introduce a framework for the generation of random graphswith a controlled size -- number of nodes, edges -- and a customizablestructure, beyond blocks and spatial ones, based on node-pair rank and atunable probability function allowing to control the amount of randomness. Weintroduce a structure zoo -- a collection of original network structures -- andconduct experiments on the small-world properties of networks generated bythose structures. Finally, we introduce an implementation as a Python librarynamed Structify-net.

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