GALLERY
You can click on the images to learn more about each network
NETS2MAPS is a project for the geometric modeling, mapping, analysis, and visualization of complex networks and big data systems.
Network geometry states that the architecture of real complex networks has a geometric origin. Under this approach, the elements of complex networks can be characterized by their positions in a latent hyperbolic underlying geometry so that the observable network topology is simply a reflection of their distances in this space.
This simple idea has led to the development of network geometry, a very general framework able to explain the most ubiquitous topological properties of real complex networks. Our models are able to explain in a very natural way highly non-trivial properties of real networks, like their self-similarity, community structure, and navigability.
The World Trade Atlas 1870-2013 Interactive Tools is an interactive graphical representation of the history of world trade from 1870 to 2013. It is based on the collection of annual world trade maps in hyperbolic space where distances incorporate the different dimensions that affect international trade, beyond mere geography (Scientific Reports 6, 33441 (2016) doi:10.1038/srep33441).
The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat, but hyperbolic.
Geometric network models are able to explain in a very natural way highly non-trivial properties of real networks, like their self-similarity, community structure, or navigability properties. You can download Mercator for creating your own networks maps in the hyperbolic plane using our embedding tool Mercator.
You can also create your own network maps in the hyperbolic space using our another embedding tool D‑Mercator
If you use Mercator or D-Mercator embeddings in a publication, do not forget to cite New J. Phys. 21, 123033 (2019) doi: 10.1088/1367-2630/ab57d2 or doi.org/10.48550/arXiv.2304.06580.
Code for inferring the intrinsic dimension of a real network in the geometric soft configuration/hyperbolic map model
Go to GitHub ReposirotyCode associated with the article "Scaling up real networks by geometric branching growth"
Go to GitHub ReposirotyData related to the article "Navigable maps of structural brain networks across species"
Go to GitHub ReposirotyCode associated with the article "Geometric description of clustering in directed networks"
Go to GitHub ReposirotyYou can click on the images to learn more about each network
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M. Ángeles SerranoICREA & Universitat de Barcelona |
Marián Boguñá EspinalUniversitat de Barcelona |
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Antoine AllardUniversité Laval |
Guillermo García-PérezUniversity of Turku |
Muhua ZhengJiangsu University |
Pedro Almagro BlancoUniversitat de Barcelona |
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Amani TahatUniversitat de Barcelona |
Elisenda OrtizUniversitat de Barcelona |
Robert JankowskiUniversitat de Barcelona |
Jasper van der KolkUniversitat de Barcelona |
marian.serrano@ub.edu | marian.boguna@ub.edu
Department of Condensed Matter Physics
Faculty of Physics, University of Barcelona
Martí i Franquès, 1 – 08028 Barcelona, Spain
