Fast cluster-based computation of exact betweenness centrality in large graphs

Journal of Big Data

Table 3 Topological information of synthetic & real graphs

	Graph	n	m	\(d_{avg}\)	\(d_{max}\)	\(cc_{avg}\)
Synthetic	Barabási-albert	6250	6249	1.999	126	0.000
	Barabási-albert	12,500	12,499	1.999	225	0.000
	Barabási-albert	25,000	24,999	1.999	344	0.000
	Barabási-albert	50,000	49,999	1.999	463	0.000
	Barabási-albert	100,000	99,999	1.999	1138	0.000
	Barabási-albert	200,000	199,999	1.999	676	0.000
	Barabási-albert	400,000	399,999	1.999	1142	0.000
	Barabási-albert	800,000	799,999	1.999	1587	0.000
Real	Web-webbase-2001 [34]	16,062	25,593	3.187	1679	0.224
	Ego-twitter [35]	22,322	31,823	2.851	238	0.072
	Internet [34]	124,651	193,620	3.107	151	0.062
	lyon-road-network¹	156,102	178,845	2.291	8	0.017
	Email-euAll [36]	224,832	339,925	3.024	7636	0.079

The names of the graphs are given in the first column, whereas the number of nodes and edges are given in the second and third columns. \(d_{avg}\) and \(d_{max}\) are the average and max degree, respectively. \(cc_{avg}\) is the average clustering coefficient
¹This dataset was supplied by the French National Institute of Geographic Information (IGN). https://www.ign.fr