Computational Social Networks

Table 4 Results C_n(w_N = 1.0, T) from Eq. (9)

From: Influence spreading model used to analyse social networks and detect sub-communities

n	w_N = 1.0
n	C_n, T = 1	C_n, T = 3	C_n, T = 5	Ranking, T = 1	Ranking, T = 3	Ranking, T = 5
1	0.173	0.677	0.872	3	3	8
2	0.075	0.378	0.756	27	8	17
3	0.317	0.810	0.896	24	17	22
4	0.238	0.744	0.888	10	22	31
5	0.031	0.031	0.031	7	31	24
6	0.204	0.700	0.880	4	24	23
7	0.244	0.759	0.895	30	23	10
8	0.214	0.807	0.906	20	7	3
9	0.089	0.473	0.800	15	10	7
10	0.264	0.751	0.896	8	4	4
11	0.089	0.473	0.800	17	30	30
12	0.031	0.031	0.031	22	27	20
13	0.168	0.678	0.869	31	20	15
14	0.140	0.590	0.868	28	15	27
15	0.215	0.736	0.886	23	28	28
16	0.132	0.576	0.861	6	6	6
17	0.214	0.807	0.906	19	19	19
18	0.031	0.031	0.031	29	29	29
19	0.191	0.678	0.877	1	13	1
20	0.230	0.737	0.888	13	1	13
21	0.095	0.402	0.769	14	14	14
22	0.214	0.807	0.906	16	16	16
23	0.208	0.782	0.896	25	25	25
24	0.287	0.804	0.897	32	32	32
25	0.103	0.548	0.835	21	9	9
26	0.065	0.249	0.586	9	11	11
27	0.315	0.744	0.886	11	21	21
28	0.211	0.707	0.884	2	2	2
29	0.191	0.678	0.877	26	26	26
30	0.238	0.744	0.888	5	5	5
31	0.214	0.807	0.906	12	12	12
32	0.103	0.548	0.835	18	18	18

Back to article page