Dataset Information


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 Erd?s-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 (and several parameterizations thereof) using 20 distance measures commonly used to compare graphs. The within-ensemble graph distance provides a new framework for developers of graph distances to better understand their creations and for practitioners to better choose an appropriate tool for their particular task.


PROVIDER: S-EPMC7735290 | BioStudies | 2020-01-01

REPOSITORIES: biostudies

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