Network comparison and the within-ensemble graph distance.

ABSTRACT: Quantifying the differences between networks is a challenging and ever-present problem in network science. In recent years, a multitude of diverse, ad hoc solutions to this problem have been introduced. Here, we propose that simple and well-understood ensembles of random networks-such as Erdős-Rényi graphs, random geometric graphs, Watts-Strogatz graphs, the configuration model and preferential attachment networks-are natural benchmarks for network comparison methods. Moreover, we show that the expected distance between two networks independently sampled from a generative model is a useful property that encapsulates many key features of that model. To illustrate our results, we calculate this within-ensemble graph distance and related quantities for classic network models (an

SUBMITTER: Hartle H 

PROVIDER: S-EPMC7735290 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature