Table 2 Precision values of different methods on 15 networks.

From: A perturbation-based framework for link prediction via non-negative matrix factorization

Precision	C. elegant	Email	Karate	Word	Jazz	USAir	Yeast	PB	NS	Router	Power	Baydry	School	SmaGri	SW
NMF1*	0.142	0.111	0.201	0.042	0.548	0.320	0.139	0.143	0.265	0.025	0.022	0.477	0.195	0.053	0.123
NMF-D1*(2)	0.171	0.143	0.234	0.042	0.615	0.362	0.165	0.170	0.298	0.016	0.023	0.539	0.216	0.068	0.153
NMF-A1*(2)	0.175	0.143	0.214	0.042	0.605	0.386	0.168	0.170	0.311	0.021	0.026	0.541	0.218	0.068	0.153
NMF2*	0.122	0.075	0.183	0.055	0.512	0.350	0.079	0.158	0.202	0.103	0.018	0.430	0.168	0.049	0.165
NMF-D2*(2)	0.185	0.144	0.201	0.079	0.593	0.445	0.169	0.234	0.303	0.067	0.029	0.520	0.182	0.118	0.246
NMF-A2*(2)	0.191	0.149	0.201	0.080	0.600	0.470	0.186	0.248	0.310	0.235	0.036	0.544	0.218	0.130	0.276
SPM*(2)	0.171	0.144	0.210	0.101	0.650	0.449	0.160	0.238	0.420	0.224	0.057	0.552	0.227	0.118	0.211
Katz(1)	0.102	0.131	0.169	0.072	0.449	0.365	0.108	0.175	0.299	0.060	0.058	0.085	0.142	0.099	0.151
LHNII(2)	0.000	0.000	0.000	0.001	0.047	0.003	0.000	0.000	0.008	0.000	0.010	0.005	0.062	0.000	0.001
ACT	0.053	0.024	0.128	0.087	0.169	0.332	0.000	0.077	0.193	0.160	0.034	0.118	0.142	0.035	0.101
TSCN(1)	0.018	0.014	0.145	0.002	0.024	0.133	0.032	0.027	0.087	0.096	0.056	0.036	0.197	0.028	0.039
Salton	0.024	0.050	0.001	0.001	0.535	0.046	0.000	0.013	0.253	0.000	0.015	0.011	0.175	0.000	0.001
Jaccard	0.028	0.071	0.001	0.002	0.521	0.064	0.000	0.017	0.252	0.000	0.007	0.010	0.180	0.000	0.001
Sorenson	0.028	0.065	0.001	0.002	0.521	0.064	0.000	0.017	0.252	0.000	0.009	0.010	0.180	0.000	0.001
HPI	0.015	0.007	0.091	0.005	0.255	0.016	0.012	0.003	0.146	0.000	0.005	0.055	0.105	0.002	0.000
HDI	0.029	0.069	0.004	0.004	0.465	0.083	0.000	0.025	0.264	0.000	0.007	0.009	0.173	0.000	0.001
LHN	0.000	0.003	0.004	0.000	0.093	0.004	0.000	0.000	0.084	0.000	0.010	0.014	0.082	0.000	0.001
CN	0.095	0.139	0.164	0.064	0.509	0.372	0.104	0.174	0.379	0.057	0.051	0.065	0.162	0.090	0.112
AA	0.112	0.151	0.163	0.067	0.524	0.396	0.104	0.172	0.563	0.038	0.030	0.063	0.148	0.103	0.131
RA	0.112	0.138	0.165	0.056	0.545	0.473	0.083	0.151	0.586	0.020	0.030	0.065	0.187	0.102	0.139
PA	0.060	0.014	0.096	0.089	0.130	0.318	0.012	0.069	0.012	0.025	0.001	0.167	0.025	0.051	0.099
LP(1)	0.100	0.131	0.169	0.072	0.495	0.370	0.107	0.175	0.299	0.059	0.054	0.071	0.113	0.095	0.128
CRA	0.116	0.157	0.199	0.038	0.557	0.391	0.123	0.177	0.481	0.062	0.033	0.085	0.210	0.118	0.147
Random	0.005	8.5e-4	0.016	0.007	0.016	0.004	2.3e-4	0.002	0.001	5e-5	5.4e-5	0.034	0.010	0.001	0.004

We compared our methods with other methods on the 15 network data sets and the precisions are returned with the average over 100 runs. The last row is the precision value of a real random predictor which is obtained by providing a ranking list that is ordered according to a random permutation of the links. For every data set, the presented links are partitioned into training set (90%) and test set (10%). The local methods are in standard character while the global methods are in italic. Number in bracket closed to a method denotes the number of tuning parameters. The best result achieved by global methods and the best result achieved by local methods on each network are boldface. Methods with an asterisk like * denote methods based on inference and methods without an asterisk denote methods based on a paradigm (in the sense that are model-based). We tune the parameters to optimize the performance of baseline methods for comparison. In our experiments, we set α = 0.0001 for LP, parameter α = 0.01 for Katz, ϕ = 0.99 and φ = 1 for LHNII, η = 0.1 for SPM, NMF − D1, NMF − A1, NMF − D2 and NMF − D2.

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Table 2 Precision values of different methods on 15 networks.

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