Manifold learning and maximum likelihood estimation for hyperbolic network embedding.

ABSTRACT: The Popularity-Similarity (PS) model sustains that clustering and hierarchy, properties common to most networks representing complex systems, are the result of an optimisation process in which nodes seek to form ties, not only with the most connected (popular) system components, but also with those that are similar to them. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract popularity-similarity trade-offs and the formation of scale-free and strongly clustered networks can be accurately described. Current methods for mapping networks to hyperbolic space are based on maximum likelihood estimations or manifold learning. The former approach is very accurate but slow; the latter improves efficiency at the cost of accuracy. Here, we analyse the

SUBMITTER: Alanis-Lobato G 

PROVIDER: S-EPMC6245200 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature