### Table 2. Reference categories for scale-free networks and general aviation.

2003

"... In PAGE 15: ... (A maximum of 24 for Lederer and Nambimadom [47] and only 7 for Yang and Kornfeld [65]). We close with Table2 , a broad category summary of our literature review of scale-free networks... ..."

### Table 3.2: The values for the exponents of the scale-free degree distri- butions of some network models. For directed models, the values for the in-degree and out-degree distributions are given individually. If the reference provides only n = |V | and k, we take m = |E| = nk/2 and round.

2003

### Table 3.2: The values for the exponents of the scale-free degree distri- butions of some network models. For directed models, the values for the in-degree and out-degree distributions are given individually. If the reference provides only n = jV j and k, we take m = jEj = nk=2 and round.

2003

Cited by 7

### Table 4 Comparison of different caching strategies on the class of scale-free networks with 100 nodes

2006

"... In PAGE 7: ... We therefore focus primarily a non- parametric measure, namely the rank the caching strategy achieved when compared to the other strategies tested (with 1 being the best rank, and 6 being the worst rank), averaged over all test cases. The results depicted in Table4 show that both evolved strategies (GPfinal and RUDF) work very well over the randomly generated range of networks in the considered class of scale-free networks. They significantly outperformed all the other strategies tested, with a slight advantage of RUDF over GPfinal.... ..."

### Table 5 Comparison of different caching strategies on the class of scale-free networks with 30 nodes

2006

### Table 6 Comparison of different caching strategies on the class of scale-free networks with 300 nodes

2006

### TABLE II. The scaling exponents characterizing the degree distribution of several scale-free networks, for which P(k) follows a power law (2). We indicate the size of the network, its average degree H20855kH20856, and the cutoff H9260 for the power-law scaling. For directed networks we list separately the indegree (H9253in) and outdegree (H9253out) exponents, while for the undirected networks, marked with an asterisk (*), these values are identical. The columns lreal , lrand , and lpow compare the average path lengths of real networks with power-law degree distribution and the predictions of random-graph theory (17) and of Newman, Strogatz, and Watts (2001) [also see Eq. (63) above], as discussed in Sec. V. The numbers in the last column are keyed to the symbols in Figs. 8 and 9.

2001

Cited by 1

### TABLE II. The scaling exponents characterizing the degree distribution of several scale-free networks, for which P(k) follows a power-law (2). We indicate the size of the network, its average degree hki and the cuto for the power-law scaling. For directed networks we list separately the indegree ( in) and outdegree ( out) exponents, while for the undirected networks, marked with a star, these values are identical. The columns lreal, lrand and lpow compare the average path length of real networks with power-law degree distribution and the prediction of random graph theory (17) and that of Newman, Strogatz and Watts (2000) (62), as discussed in Sect. V. The last column identi es the symbols in Figs. 8 and 9.

2001

Cited by 1

### Table 3 Correlation between low-level parameters and average of measured MSE of the NARMA task for Scale-free networks. Each parameter is measured for each network size and then averaged.

"... In PAGE 20: ... Since the models which were considered for small-world and random networks may give disconnected networks, some of mentioned parameters could not be calculated for all such graphs. Table3 presents the correlation between average of measured MSE of the NARMA task and calculated low-level parameters for scale-free reservoirs. The result for small-world and random reservoirs are presented in table 4.... ..."