Significance-based multi-scale method for network community detection and its application in disease-gene prediction.

ABSTRACT: Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-scale structures of complex networks. In this paper, we investigated a statistical measure of interest for community detection, Significance [Sci. Rep. 3 (2013) 2930], and analyzed its critical behaviors based on the theoretical derivation of critical number of communities and the phase diagram in community-partition transition. It was revealed that Significance exhibits far higher resolution than the traditional Modularity when the intra- and in

SUBMITTER: Hu K 

PROVIDER: S-EPMC7083276 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature