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Accord to the theory above, we designed the RWS Random Walk Samplingg) method to detect the overlapping communities, utilizing the random walk method to find the closest friends for each node.
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In [ 1], the authors utilize both the random walk and random walk with restart to rank genes with respect to their likelihood of being a member of each cancer module through the functional interaction network globally and interactions between genes in each cancer module locally.
To demonstrate advantages of the proposed method, its performance was compared with that of the random walk method [ 13], which utilized a node's global neighborhood in a network to rank genes.
Stratified cross-validation was utilized for performance comparison between the proposed approach and the random walk method [ 13].
The random walk, compared to other popular hierarchical or seed clustering methods, utilizes the structure of the network to build distance metrics, and Walktrap optimizes the community search using the graph-theoretic concept of modularity.
In the random walk method or RW [27, 28], a virtual walking particle performs a random walk on a 2-dimensional image lattice with a four-neighbor system.
It was the random walk of Herbert Robbins.
Consider, the random walk model in [40] was designed to walk over the entire network.
Thus, target guiding is added in the random walk.
We formalize the game using the random walk framework.
The unknown system changes according to the random walk model.
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