Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

Applied Network Science

Table 1 Basic network statistics

City	\|V\|	\|E\|	p ₁	p ₂	p ₃	p ₄	p _>4	C	L
Aichi	259,915	402,465	.065	.070	.574	.285	.006	0.04	98.71
Barcelona	66,790	99,387	.103	.031	.659	.201	.006	0.06	53.07
Brasilia	95,811	136,955	.133	.025	.694	.146	.002	0.04	92.94
Chiba	227,791	335,564	.116	.003	.703	.177	.002	0.04	99.73
Ibaraki	172,892	263,075	.083	.002	.708	.205	.003	0.04	111.02
Kanagawa	295,151	402,576	.192	.051	.597	.155	.005	0.04	129.17
Kyoto	88,800	128,601	.099	.090	.633	.174	.004	0.07	103.43
New York	325,962	466,510	.159	.033	.600	.204	.004	0.04	103.14
Osaka	261,939	375,750	.133	.066	.606	.190	.006	0.05	91.32
Saitama	230,408	351,811	.084	.001	.695	.217	.003	0.04	81.04
San Francisco	110,700	156,821	.173	.037	.583	.199	.009	0.05	79.43
Seoul	103,444	150,822	.111	.078	.605	.198	.008	0.04	43.99
Shizuoka	110,925	162,322	.121	.070	.576	.228	.005	0.05	83.09
Tokyo	340,919	485,858	.163	.034	.599	.197	.006	0.03	126.19
Washington D.C.	24,564	38,053	.096	.028	.571	.293	.012	0.05	51.89