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In this study, we categorize networks into personal networks and network structure of farmers' groups.
One way to categorize networks is by the number of types of actors they contain.
They can be used to categorize networks into superfamilies 5 or to identify application protocols.
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The key idea is to categorize network traffic into different classes and embed the same class parameter as a configuration knob in routing.
This method can obtain the minimum enclosing ball of the sample set according to updates of the center and the radius of sphere and categorize network intrusions according to distributions of support vectors.
We developed a new algorithm (see Methods) to categorize network motifs into "motif modes" on the basis of the topology of the GO terms annotated for motif nodes.
The binary coding of perceived effectiveness categorizing networks as either effective or ineffective holds two important limitations.
Identifying and categorizing network traffic by application type is challenging because of the continued evolution of applications, especially of those with a desire to be undetectable.
We categorize opportunistic networks currently known in the literature as class 1 opportunistic networks that use opportunistically only communication resources, and class 2 opportunistic networks or oppnets that use opportunistically all kinds of resources, including not only communication but also computation, sensing, actuation, storage, etc.
Specifically, we categorized social networks (the three people with whom the respondent discusses politics) into five groups: composed of only Hillary Clinton voters; both Clinton and not sure/other voters; both Clinton and Donald Trump voters; both Trump and not sure/other voters; and only Trump voters.
We have also included strategic flexibility categorized into network and organizational design flexibility dimensions.
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