NETS2MAPS is a web platform for the geometric 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 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, or navigability properties.

Mercator & community detection

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. Click LOGIN or REGISTER HERE for creating your own networks maps in the hyperbolic plane using our on-line embedding tool Mercator. See the user manual for a guide on how to carry out embeddings of your own networks using our online tool Mercator and how to perform community detection and visualization of networks in the hyperbolic plane. Download Mercator in case that your network has >5000 nodes. If you use Mercator embeddings in a publication, do not forget to cite New J. Phys. 21, 123033 (2019) doi: 10.1088/1367-2630/ab57d2. We also provide tools for the detection of geometric community structure, Scientific Reports 6, 33441 (2016) doi:10.1038/srep33441.