"... In PAGE 9: ... Thus, we simply exclude these nodes from the computation of Cactual. Table3 presents ranges for the clustering coe cient and mean path length for several re- cently captured snapshots of the Gnutella top-level overlay as well as the mean values from three random graphs with the same number of vertices and edges (i.e.... ..."

"... In PAGE 29: ...3.2 RP-Learn Table5 presents the network outputs for an rp-learn agent from generation 200 of a simulation in small-world with 100 epochs of interaction. Recall that rp-learn agents use a network that takes as input a representation of both an environment state and an action.... ..."

"... In PAGE 7: ...THE NEUROSCIENTIST Small-World Brain Networks nodes). It follows that the reported topology of brain func- tional networks could depend considerably on the number of regions included, the chosen measure of association, and the thresholding rule (see Table2 for a comparison). The relative merits of the various methodological options remain to be fully evaluated.... ..."

"... In PAGE 11: ... Therefore, the more distinct the small world prop- erties of a network are, the bigger IndexSN of the network becomes. Table2 shows the average shortest path, L and clustering coefficient, C for giant components extracted from each of the 25 web service networks, compared to random graphs with the same number of nodes and average number of edges per node. Note that we treat all edges of each network as undirected and un- weighted3.... ..."

"... In PAGE 10: ...We consider four datasets summarized in Table1 . The first two have previously been an- alyzed using latent position and latent position cluster models, and we compare the model fits to those previously obtained.... In PAGE 18: ... Both have markedly non-Gaussian sociality effects, and the first does not have multiple groups. The strength of the small world effects for the undirected networks is given by the clus- tering coefficient values in Table1 . The clustering coefficient for a purely random network with 150 actors and the same density as these networks is 2.... ..."

"... In PAGE 10: ...We consider four datasets summarized in Table1 . The first two have previously been an- alyzed using latent position and latent position cluster models, and we compare the model fits to those previously obtained.... In PAGE 18: ... Both have markedly non-Gaussian sociality effects, and the first does not have multiple groups. The strength of the small world effects for the undirected networks is given by the clus- tering coefficient values in Table1 . The clustering coefficient for a purely random network with 150 actors and the same density as these networks is 2.... ..."

"... In PAGE 27: ...3.1 FF-Learn Table4 presents the outputs of the networks of a #0B-learn agent from the 200th generation of a simulation in small-world with 100 epochs of interaction, along with a summary of the actions it performs. Recall that the action network is given as input a representation of the current environment state, and the output of the agent apos;s action network is interpreted as a binary number, which is the action performed.... ..."