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A random walk procedure is then applied to predict the atmospheric delays accurately over a short time span.
Comparing to the normal random walk procedure defined in "Basic definitions and the random walk procedure" section, LRW involves inflation and normalization operations in each walking step.
The random walk procedure is equivalent to a discrete-time stationary Markov chain process.
Starting from the seed vertex, a normal random walk procedure will eventually explore the whole graph.
Moreover, we construct two new algorithmic measures of network's navigability which are based on the random walk procedure.
We use the limited random walk procedure to find attractor vertices in a graph and use them as features to cluster the vertices.
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The accuracy of any criteria-based clustering method (or those combined with the random walk procedures) is greatly affected by the chosen clustering fitness function.
These methods exploit biological networks such as the functional gene interactions network and disease similarity network; they infer gene disease connections by using random walk procedures on different biological networks or computing a similarity measure between nodes.
For a given number of generations, if the best conformation in the new population is not better than the best in the current population, our algorithm then resorts to a random-walk procedure (Line 17) to diversify the new population.
The cyan curve corresponds to a theoretical clock model consisting of a random walk and an integrated random walk process.
We call the aforementioned procedure the limited random walk (LRW) procedure.
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