Table 1 Illustration of properties of networks.

From: Predicting missing links in complex networks based on common neighbors and distance

Networks

N

m

c

c n

c r

〈d〉

〈k〉

r

H

Karate

34

78

0.571

0.859

0.793

0.771

2.408

4.588

−0.476

1.693

Dolphins

62

159

0.259

0.761

0.710

0.715

3.357

5.129

−0.044

1.327

Polbook

105

441

0.488

0.959

0.937

0.927

3.079

8.400

−0.128

1.421

Word

112

425

0.173

0.725

0.694

0.672

2.536

7.589

−0.129

1.815

Neural

297

2148

0.292

0.945

0.913

0.916

2.455

14.465

−0.163

1.801

Circuit

512

819

0.055

0.137

0.118

0.115

6.858

3.199

−0.030

1.259

Email

1133

5451

0.220

0.776

0.734

0.733

3.606

9.622

0.078

1.942

Power

4941

6594

0.080

0.208

0.179

0.176

18.989

2.669

0.003

1.450

  1. Parameters are measured in original networks G except and cr in G′ = G − EP, where .
  2. cn: CN coefficient; ; 〈d〉: average distance; 〈k〉: average degree; c: clustering coefficient; r: assortativity coefficient (see Methods section); : degree heterogeneity. The values of cr and are the average of 20 realizations to randomly remove EP for each network every time.
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