Table 1.

Link prediction accuracy measured by precision on the 10 real networks

Precision	Jazz	Metabolic	Neural	USAir	Food web	Hamster	NetSci	Yeast	Email	Router
SPM	0.677	0.354	0.168	0.451	0.561	0.469	0.334	0.166	0.158	0.357
CN	0.506	0.137	0.095	0.374	0.073	0.061	0.329	0.109	0.149	0.027
AA	0.525	0.190	0.105	0.394	0.075	0.061	0.334	0.121	0.150	0.026
RA	0.541	0.267	0.104	0.455	0.076	0.054	0.541	0.090	0.148	0.027
Katz	0.546	0.147	0.107	0.379	0.181	0.108	0.370	0.061	0.149	0.120
HSM	0.326	0.100	0.073	0.216	0.249	0.202	0.303	0.081	0.134	0.309
SBM	0.410	0.197	0.143	0.335	0.460	0.275	0.177	0.122	0.094	0.176

We compare our method, SPM, to six well-known methods presented in Materials and Methods. For each real network, 10% of its links will be randomly selected to constitute the probe set, and the rest of the links constitute the training set. Prediction accuracy is measured by precision. We set $p^H=0.1$ for SPM. For the parameter-dependent Katz index, the present results correspond to the optimal parameter subject to the highest precision. The highest value for each network is in boldface.