A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

Table 3 The properties of real networks

Network	N	L	<k>	E ¹ ₁	E ² ₁	E ³ ₁	E ¹ ₂	E ² ₂	E ³ ₂
Regulatory
TRN-EC	418	519	1.24	0.8119	0.8230	0.7717	0.5577	0.5963	0.6082
Yeast	688	1079	1.57	0.8083	0.7314	0.7141	0.5460	0.5798	0.5880
Foodweb
Chesapeake	39	177	4.54	0.3049	0.2331	0.2318	0.5521	0.7331	0.8374
ChesUpper	37	215	5.81	0.2777	0.2450	0.2164	0.5502	0.6999	0.8304
Florida	128	2106	16.45	0.2849	0.1923	0.1255	0.5383	0.7506	0.8390
Electronic circuits
s420	252	399	1.58	0.3466	0.1761	0.1666	0.5484	0.7315	0.7434
s208	122	189	1.55	0.3499	0.2106	0.2021	0.5582	0.6967	0.7826
s838	512	819	1.60	0.3563	0.1821	0.1780	0.5438	0.6797	0.6899
Airports
USAir97	332	2126	6.40	0.4410	0.3047	0.2870	0.5477	0.6545	0.6815
Trust
colledge_student	32	96	3.00	0.3285	0.3382	0.2103	0.5387	0.7457	0.8649
Words
glossGT	72	118	1.64	0.7010	0.6441	0.5630	0.5442	0.6192	0.6701
Web
Polblogs	1490	19,090	12.81	0.5414	0.4605	0.4572	0.5953	0.6430	0.6459
Genetic
Rattusp	2640	4268	1.62	0.7385	0.6808	0.6756	0.5990	0.6374	0.6397
Celegants	3879	8482	2.19	0.7250	0.6340	0.6315	0.5994	0.6370	0.6400
Plasmodium	1203	2522	2.10	0.5909	0.4489	0.4468	0.6044	0.6709	0.6766

Here, we list the network types, network name, quantity of nodes in network (N), quantity of edges in network (L), the average degree of network <k>, the average ratio of drives to the targets by using Gao’s method (E¹₁) and our TCOA without MCMC samplings (E²₁) and our TCOA (E³₁) respectively and average ratio of drivers within the set of constrained (or pre-selected) nodes to all the identified drivers by using Gao’s method (E¹₂) and our TCOA without MCMC samplings (E²₂) and our TCOA (E³₂) respectively. The more detail descriptions of the real-world networks including the network types, names and references, quantity of nodes and edges and brief description and the downloaded websites, are shown in Additional file 2: Table S1

ISSN: 1471-2164