Structify-Net: Random Graph generation with controlled size and customized structure
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 ran...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Peer Community In
2023-10-01
|
| Series: | Peer Community Journal |
| Subjects: | |
| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.335/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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.
|
|---|---|
| ISSN: | 2804-3871 |