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And the second method is graph partitioning base on sparse cut.
The exchange of a chunk of nodes that are identified using a sparse cut metric was studied in Kurve et al. (2011b).
On the other hand, if the parents are hybridized at a sparse cut, the connectivity among vertices within each partition are more localized.
For pathogenic diseases, drugs are designed to act on the pathogen directly, and drug targets are those enzymes crucial for the survival and growth of the pathogen, which can be identified by FBA-based growth simulation or sparse cut strategies [ 8, 18, 20].
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The dense regions discovered at this phase provide us (1) the set of edges within each dense region; and (2) sparse cuts between disjoint dense regions.
To generate these sparse cuts, we contract each dense region identified in Algorithm 2 to a single vertex, and then generate weighted (by the number of vertices in each dense region) balanced partitioning of the vertices at random.
The Sparsest Cut (SC) is often introduced as a weak dual of the Maximum Concurrent Flow problem (MCF).
In [25,26], an (mathcal {O}(sqrt {n}))-approximation algorithm is presented for the sparsest cut, edge expansion, balanced separator, and graph conductance problems, all of which are based on the notion of graph partitioning.
School of Engineering and Applied Science, Southern Methodist University] provide the basis for partitioning a social network by way of sparsest cuts and/or maximum concurrent flow.
We show that good community structures can be obtained by partitioning a social network in a succession of divisive sparsest cuts.
A network flow algorithm based on fundamental principles of graph theory is introduced to identify the sparsest cuts and an underlying hierarchical community structure of the network via maximum concurrent flow.
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