Sentence examples for sample of networks from inspiring English sources

Since this specification uses data at the network level, estimation will require a large sample of networks in order to recover precise estimates of the parameters, even in the absence of endogeneity from network formation and mismeasurement of the network.

The CUG test for transitivity[17, 18] evaluates the observed degree of transitivity (the proportion of potential transitive triads that are actually closed) against a null distribution computed on a random sample of networks without transitivity.

However, for the sample of networks used here, as noted above, the mean node degree 〈k〉i is approximately constant.

Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n.

To quantify this approximation, we performed a linear regression on log-transformed quantities (just as for the real networks) over the typical range of n encountered in our sample of networks, 102≤n≤107, and found a linear fit, with r2 within 10−5 of unity.

Subsequently, a Monte Carlo process generates a sample of networks of specific character.

Therefore, consensus on factors important to risk judgments from a sample of network professionals may inform risk metric designers.

The 1-h sample of network traffic was sufficient to capture connections from almost all the different clients observed over a longer period of time.

Hence, whether or not context was present, our sample of network professionals believed that dimensions of network risk should include both organizational network infrastructure (the preparedness for attack) and the threat/adversary (the attack).

The purpose of this research was to examine first, what factors are important to network risk judgments (Study 1) and second, how risky/safe each factor is judged (Study 2) by a sample of network professionals.

Figure 1 a defines a sample of network model where blue bold circles define cluster heads; we may see each sensor node's neighbor, cluster area, and their network area.