Quantifying dissimilarities between heterogeneous networks with community structure

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Quantifying dissimilarities between networks is a challenging problem. Current metrics may assume homogeneous distribution of nodal degrees or ignore network community structure. The proposed measure efficiently compares heterogeneous networks with communities, considering probability distribution functions, and returns non-zero values only for non-isomorphic networks.