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The distance between a pair of graph models has been described using a graph edit distance measure in [125].
Alternatively a distance or similarity measure may be described between a pair of these models using a graph edit distance measure.
Alternatively a distance or similarity measure can be described between a pair of these models using a graph edit distance measure as has been described in [125].
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We addressed this problem using a graph theoretical approach.
We could now compute the similarity between the representative graphs using a graph kernel.
The major reason for us to use a graph database was their support for graph algorithms.
Since scaffolds are graphs, using the graph edit distance might look tempting.
This method uses the graph edit distance (GED) score as a test statistic.
We directly define our graph by using a bidirected graph.
To quantify the effects of prior knowledge incorporation, we use graph edit distance [ 24] between two adjacency matrices as a measurement for the dissimilarity of two graphs.
Data was analysed using a hermeneutic editing approach.
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