Displaying similar documents to “Graph fibrations, graph isomorphism, and PageRank”

Two operations of merging and splitting components in a chain graph

Milan Studený, Alberto Roverato, Šárka Štěpánová (2009)

Kybernetika

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In this paper we study two operations of merging components in a chain graph, which appear to be elementary operations yielding an equivalent graph in the respective sense. At first, we recall basic results on the operation of feasible merging components, which is related to classic LWF (Lauritzen, Wermuth and Frydenberg) Markov equivalence of chain graphs. These results are used to get a graphical characterisation of factorisation equivalence of classic chain graphs. As another example...

Centers in line graphs

Martin Knor, Ľudovít Niepel, Ľubomír Šoltés (1993)

Mathematica Slovaca

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The periphery graph of a median graph

Boštjan Brešar, Manoj Changat, Ajitha R. Subhamathi, Aleksandra Tepeh (2010)

Discussiones Mathematicae Graph Theory

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The periphery graph of a median graph is the intersection graph of its peripheral subgraphs. We show that every graph without a universal vertex can be realized as the periphery graph of a median graph. We characterize those median graphs whose periphery graph is the join of two graphs and show that they are precisely Cartesian products of median graphs. Path-like median graphs are introduced as the graphs whose periphery graph has independence number 2, and it is proved that there are...

How the result of graph clustering methods depends on the construction of the graph

Markus Maier, Ulrike von Luxburg, Matthias Hein (2013)

ESAIM: Probability and Statistics

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We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph clustering algorithm to find a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) influences the outcome of the final clustering result. To this end we study the convergence of cluster quality...