Quantifying dissimilarities between heterogeneous networks with community structure

Xin-Jian Xu, Cheng Chen and J.F.F. Mendes

Physica A: Statistical Mechanics and its Applications, 2022, vol. 588, issue C

Abstract: Quantifying dissimilarities between networks is a fundamental and challenging problem in network science. Current metrics for network comparison either assume the homogeneous distribution of nodal degrees or ignore the community structure of the network. Here we propose an efficient measure for comparing heterogeneous networks with communities from the perspective of probability distribution functions, which incorporates the nodal distance distribution, the clustering coefficient distribution and the alpha centrality distribution. Comparison between community benchmarks shows that the proposed measure returns non-zero values only when the networks are non-isomorphic.

Keywords: Complex networks; Network comparison; Dissimilarity measure (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:588:y:2022:i:c:s0378437121008475

DOI: 10.1016/j.physa.2021.126574

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