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An Analysis of Correlation and Comparisons Between Centrality Measures in Network Models

Javad Mohamadichamgavi
Javad Mohamadichamgavi
, 
Mahdi Hajihashemi
Mahdi Hajihashemi
 et 
Keivan Aghababaei Samani
Keivan Aghababaei Samani
   | 20 janv. 2024
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Publié en ligne: 20 janv. 2024
Pages: 1 - 21
DOI: https://doi.org/10-21307/joss-2024-001
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© 2024 Javad Mohamadichamgavi et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Correlations between centrality measures in ER network with size of 500 as a function of $p$. (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in ER network with size of 500 as a function of $p$. (a) Pearson correlation (b) Spearman correlation.

Figure 2.

Correlations between centrality measures in ER network with connection probability p=0.1 as a function of size of network. (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in ER network with connection probability p=0.1 as a function of size of network. (a) Pearson correlation (b) Spearman correlation.

Figure 3.

Correlations between centrality measures in BA network N=500 as a function of m (2m is average degree). (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in BA network N=500 as a function of m (2m is average degree). (a) Pearson correlation (b) Spearman correlation.

Figure 4.

Correlations between centrality measures in BA network m=1 as a function of N (network size).(a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in BA network m=1 as a function of N (network size).(a) Pearson correlation (b) Spearman correlation.

Figure 5.

Correlations between centrality measures in SW network with N=400 as a function of k (average degree). The rewiring probability is constant pWS=0.3. (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in SW network with N=400 as a function of k (average degree). The rewiring probability is constant pWS=0.3. (a) Pearson correlation (b) Spearman correlation.

Figure 6.

Correlations between centrality measures in SW network with k=2 as a function of N (network size). The rewiring probability is constant pWS=0.3. (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in SW network with k=2 as a function of N (network size). The rewiring probability is constant pWS=0.3. (a) Pearson correlation (b) Spearman correlation.

Figure 7.

Correlations between centrality measures in SW network N=400 as a function of pWS (rewiring probability). The average degree is constant 10. (a) Pearson correlation (b) Spearman correlation.
Correlations between centrality measures in SW network N=400 as a function of pWS (rewiring probability). The average degree is constant 10. (a) Pearson correlation (b) Spearman correlation.

Figure 8.

(Color online) The effect of global topological properties of network models on average Pearson correlation of centralities. The dots represent the average of six pairs of correlation between measures for a network with random parameters. Red is ER, Blue is BA, and Green is SW network.
(Color online) The effect of global topological properties of network models on average Pearson correlation of centralities. The dots represent the average of six pairs of correlation between measures for a network with random parameters. Red is ER, Blue is BA, and Green is SW network.

Figure 9.

(Color online) The effect of global topological properties of network models on average Spearman correlation of centralities. The dots represent the average of six pairs of correlation between measures for a network with random parameters. Red is ER, Blue is BA, and Green is SW network.
(Color online) The effect of global topological properties of network models on average Spearman correlation of centralities. The dots represent the average of six pairs of correlation between measures for a network with random parameters. Red is ER, Blue is BA, and Green is SW network.

Figure 10.

(Color online) The effect of construction parameters of network models on global topological properties of networks. Red is ER, Blue is BA, and Green is SW network.
(Color online) The effect of construction parameters of network models on global topological properties of networks. Red is ER, Blue is BA, and Green is SW network.
eISSN:
1529-1227
Langue:
Anglais
Périodicité:
Volume Open
Sujets de la revue:
Social Sciences, other
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