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This approach represents the design priority and related design constraints within a product using a structural graph, helping designers to create variant design solutions in a product family for different market requests.
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We could now compute the similarity between the representative graphs using a graph kernel.
We directly define our graph by using a bidirected graph.
We addressed this problem using a graph theoretical approach.
The major reason for us to use a graph database was their support for graph algorithms.
We summarize all the graphs in D using an edge weighted graph.
In chemical graph theory, we usually use a graph to represent a molecule graph.
This research proposes a model using graph theory which holistically considers all the major enablers of resilience and their interrelationships for analysis using an Interpretive Structural Modeling approach.
Throughout this manuscript we use a graph theory definition of a subtree, i.e. "A tree G' whose graph vertices and graph edges form subsets of the graph vertices and graph edges of a given tree G" [ 18].
Then, we define the entropy of G by <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0003079.e014.PNG" class= inline-graphic"/> As outlined in [28], we recall that the process of defining information functionals and, hence, the entropy of a graph by using structural properties or graph-theoretical quantities is not unique.
The method we want to use is mainly based on the principle to assign a probability value to each vertex in a graph by using a certain information functional for quantifying structural information in a graph and, hence, for determining its entropy.
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