A unified data representation theory for network visualization, ordering and coarse-graining.

ABSTRACT: Representation of large data sets became a key question of many scientific disciplines in the last decade. Several approaches for network visualization, data ordering and coarse-graining accomplished this goal. However, there was no underlying theoretical framework linking these problems. Here we show an elegant, information theoretic data representation approach as a unified solution of network visualization, data ordering and coarse-graining. The optimal representation is the hardest to distinguish from the original data matrix, measured by the relative entropy. The representation of network nodes as probability distributions provides an efficient visualization method and, in one dimension, an ordering of network nodes and edges. Coarse-grained representations of the input network enable

SUBMITTER: Kovacs IA 

PROVIDER: S-EPMC4642569 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature