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Table 3 The properties of real networks

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

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
  1. 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