Approximating clustering coefficient and transitivity.
Schank, Thomas, Wagner, Dorothea (2005)
Journal of Graph Algorithms and Applications
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Schank, Thomas, Wagner, Dorothea (2005)
Journal of Graph Algorithms and Applications
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Görke, Robert, Gaertler, Marco, Hübner, Florian, Wagner, Dorothea (2010)
Journal of Graph Algorithms and Applications
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Jan Pelikán (2013)
Kybernetika
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We deal with a logistic problem motivated by a case study from a company dealing with inland transportation of piece goods in regular cycles. The problem consists in transportation of goods among regional centres – hubs of a network. Demands on transportation are contained in a matrix of flows of goods between pairs of hubs. The transport is performed by vehicles covering the shipping demands and the task is to design a cyclical route and to place a depot for each vehicle. The route...
Gansner, Emden, Hu, Yifan (2010)
Journal of Graph Algorithms and Applications
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Dragoš Cvetković, Mirjana Čangalović, Vera Kovačević-Vujčić (2004)
Zbornik Radova
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Markowsky, Greg, Koolen, Jacobus (2010)
The Electronic Journal of Combinatorics [electronic only]
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Tatjana Aleksić, I. Gutman, M. Petrović (2007)
Bulletin, Classe des Sciences Mathématiques et Naturelles, Sciences mathématiques
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Dragan Matić, Milan Božić (2012)
The Teaching of Mathematics
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Philipp Moritz, Jörg Reichardt, Nihat Ay (2014)
Kybernetika
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Given a fixed dependency graph that describes a Bayesian network of binary variables , our main result is a tight bound on the mutual information of an observed subset of the variables . Our bound depends on certain quantities that can be computed from the connective structure of the nodes in . Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.