Algorithms of the first category are based on matrix theory concepts, while algorithms of the second one are based on graph theory concepts.
The main graph clustering formulations are based on graph cut and partitioning problems [39, 40].
The transformation algorithms are based on graph transformations; tool support has been developed.
Both the specification of the formalism and the model manipulation are based on graph grammars.
The knowledge representation is symbolized with the conceptual graphs formalism and the reasoning mechanisms are based on graph operations.
According to this codification, three following rules are formed which are based on graph theory to identify and correct infeasible individuals whenever appeared in the computational process.
The most prominent stereo algorithms for minimizing the global energy function are based on graph cuts [7] and belief propagation [8] optimization methods.
Finally, there are dynamic programming algorithms, which are based on graph decompositions and results from (parameterized) complexity analysis.
The most sophisticated of these network properties are based on graph spectra (Thorne and Stumpf, 2012; Wilson and Zhu, 2008).
The approach is based on graphs, morphisms and operations from category theory and exploits triple graphs to annotate model elements with pattern roles.
