Node-weighted centrality: a new way of centrality hybridization

Singh, Anuj; Singh, Rishi Ranjan; Iyengar, S. R. S.

doi:10.1186/s40649-020-00081-w

Computational Social Networks

Table 1 Considered real-world networks

From: Node-weighted centrality: a new way of centrality hybridization

Instance name	n	m	Avg. deg.	Density	\(\hat{C}\)	Assortativity (D)	Max clique	Network type
Chicago Road [36]	1467	1298	1.7696	0.0012	0.0001	\(-\) 0.5049	2	Transport network
Chilean Power Grid [38]	347	444	2.5591	0.0074	0.0865	\(-\) 0.0773	4	Energy network
Euroroad [36]	1174	1417	2.414	0.0021	0.0167	0.1267	3	Transport network
London Metro [34]	266	308	2.3158	0.0087	0.0363	0.1531	3	Transport network
Madrid Metro [34]	209	240	2.2967	0.011	0.0056	0.1776	3	Transport network
Mexico Metro [34]	147	164	2.2313	0.0153	0.0034	\(-\) 0.1105	3	Transport network
Minnesota Road [31]	2642	3303	2.5004	0.0009	0.016	\(-\) 0.1848	3	Transport network
Moscow Metro [34]	134	156	2.3284	0.0175	0.0174	0.4492	3	Transport network
New York Metro [34]	433	475	2.194	0.0051	0.0173	\(-\) 0.0297	3	Transport network
Oldenburg Road [32]	6105	7029	2.3027	0.0004	0.0108	0.0554	3	Transport network
Openflights [34]	2939	15677	10.6683	0.0036	0.4526	0.0509	22	Transport network
Osaka Metro [34]	108	123	2.2778	0.0213	0.0001	0.1965	2	Transport network
p2p-Gnutella08 [37]	6301	20777	6.5948	0.001	0.0109	0.0356	5	Internet peer-to-peer network
Paris Metro [34]	299	356	2.3813	0.008	0.0204	\(-\) 0.0297	3	Transport network
as20000102 [37]	6474	13895	4.2926	0.0007	0.2522	\(-\) 0.1704	10	Autonomous systems graph
Seoul Metro [34]	392	437	2.2296	0.0057	0.006	0.0313	3	Transport network
Shanghai Metro [34]	148	158	2.1351	0.0145	0.0029	\(-\) 0.0318	3	Transport network
Tokyo Metro [34]	217	262	2.4147	0.0112	0.0237	0.1983	3	Transport network
US Air [35]	332	2126	12.8072	0.0387	0.6252	\(-\) 0.2079	22	Transport network
US Power Grid [39]	4941	6594	2.6691	0.0005	0.0801	0.0035	6	Energy network
NRPG data [33]	246	373	3.0325	0.0124	0.1071	\(-\) 0.0932	3	Energy network

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