A network analysis to identify lung cancer comorbid diseases

Applied Network Science

Table 4 Modularity measurement

Algorithm	Newman Girvan	Erdos Renyi	Link	Density	Z
Fluid	0.548	0.510	0.121	24.106	1.207
Belief	0.701	0.760	0.140	59.499	1.580
CPM	− 0.010	0.000	0.000	− 1964	− 0.098
Chinese whispers	0.711	0.773	0.135	70.804	1.715
DER	0.417	0.491	0.143	26.175	0.844
Eigenvector	0.707	0.779	0.140	65.678	1.601
EM	0.549	0.591	0.136	34.864	1.126
ga	0.649	0.723	0.124	54.424	1.587
Girvan Newman	0.703	0.762	0.142	59.117	1.571
Greedy modularity	0.678	0.711	0.132	61.066	1.593
Kcut	0.000	0.007	0.144	6.587	0.000
Label propagation	0.710	0.781	0.134	73.272	1.716
Leiden	0.707	0.757	0.135	66.314	1.683
Louvain	0.711	0.773	0.135	70.804	1.715
Markov Clustering	0.707	0.786	0.140	68.667	1.603
RBER Pots	0.702	0.790	0.139	63.449	1.597
RB Pots	0.711	0.773	0.135	70.804	1.715
Significance	0.622	0.719	0.118	5.528	1.567
Spinglass	0.710	0.781	0.134	73.272	1.716
Surprise	0.697	0.780	0.132	59.834	1.691
Walktrap	0.707	0.786	0.140	68.667	1.603