Citation

BibTex format

@article{Evans:2022:10.1038/s42005-022-00949-5,
author = {Evans, TS and Chen, B},
doi = {10.1038/s42005-022-00949-5},
journal = {Communications Physics},
pages = {1--11},
title = {Linking the network centrality measures closeness and degree},
url = {http://dx.doi.org/10.1038/s42005-022-00949-5},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We propose a non-linear relationship between two of the most importantmeasures of centrality in a network: degree and closeness. Based on ashortest-path tree approximation, we give an analytic derivation that shows theinverse of closeness is linearly dependent on the logarithm of degree. We showthat our hypothesis works well for a range of networks produced from stochasticnetwork models including the Erdos-Reyni and Barabasi-Albert models. We thentest our relation on networks derived from a wide range of real-world dataincluding social networks, communication networks, citation networks, co-authornetworks, and hyperlink networks. We find our relationship holds true within afew percent in most, but not all, cases. We suggest some ways that thisrelationship can be used to enhance network analysis.
AU  - Evans,TS
AU  - Chen,B
DO  - 10.1038/s42005-022-00949-5
EP  - 11
PY  - 2022///
SN  - 2399-3650
SP  - 1
TI  - Linking the network centrality measures closeness and degree
T2  - Communications Physics
UR  - http://dx.doi.org/10.1038/s42005-022-00949-5
UR  - http://arxiv.org/abs/2108.01149v2
VL  - 5
ER  -
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