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Figure 4 plots the frequency of the generated most probable subgraphs for both these data sets.
So, we repeat this process for each of the n most probable subgraphs.
It is worth mentioning that extending it to find the n most probable subgraphs is trivial.
In this paper, we develop a method that finds the most probable subgraphs efficiently.
Our experiments comprehensively demonstrate that most probable subgraphs often have very large frequency values as well.
It shows that the most probable subgraphs found in eukaryote are also frequent in prokaryote.
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For each of the most probable subgraph, we augment an edge to it at a time if the new subgraph is both connected and frequent.
Table 3 presents the average frequency value of the most probable subgraph discovered by SiS in D and that of implanted subgraph.
For relatively larger subgraphs (k ≥ 15), frequency of the most probable subgraph was just above the frequency of the implanted ones.
By definition, the score value of the most probable subgraph is less than or equal to that of all the subgraphs in D with the same number of edges.
We observe that the frequency of the most probable subgraph found was at least as good as that of the implanted one on the average for all parameter settings.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com