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The focus is on scalability and efficiency of large scale networks simulation.
The approach leads to an algorithm which has been applied successfully on large scale networks.
In large scale networks, we assume that λ → ∞.
The same trends carry on while testing on large scale networks (the NSF network).
The human brain consists of multiple interconnected large scale networks.
Pajek is suitable for large scale networks with thousands or even million of nodes and vertices.
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His research lies at the intersection of learning theory, large-scale networks, and optimum information processing.
We will follow, where breach probabilities for large-scale networks are obtained from a standard interdependent security (IDS) setup.
Reprinted in Large-Scale Networks: Theory and Design, F. T. Boesch (ed)., IEEE Press, N. Y., 1976.
... T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions.
The researchers argued that understanding how trust plays into such large-scale networks may foster more successful innovations for society.
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