2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed 2008
DOI: 10.1109/snpd.2008.17
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The Complexity of the Evolution of Graph Labelings

Abstract: We study the Graph Relabeling Problem-given an undirected, connected, simple graph G = (V, E), two labelings L and L ′ of G, and label flip or mutation functions determine the complexity of transforming or evolving the labeling L into L ′ . The transformation of L into L ′ can be viewed as an evolutionary process governed by the types of flips or mutations allowed. The number of applications of the function is the duration of the evolutionary period. The labels may reside on the vertices or the edges. We prove… Show more

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Cited by 5 publications
(4 citation statements)
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References 22 publications
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“…However, when we tested our multivariate data set, the first principal component (PC1) from PCA can only weakly explain for the data variability, that is, only 32% of the variance has been explained; PC2 and PC3 each both have 20% of the variance explained (see Figure S1 in the Supporting Information). So instead, co-occurrence patterns were analyzed using graph theory and a selection scheme based on Pearson product-moment correlation coefficient R . We set a condition for determining the presence of strong collinearity: for | R | > 0.5, two distinct variables are considered strongly collinear; otherwise, they are treated to be independent from each other.…”
Section: Computational Detailsmentioning
confidence: 99%
“…However, when we tested our multivariate data set, the first principal component (PC1) from PCA can only weakly explain for the data variability, that is, only 32% of the variance has been explained; PC2 and PC3 each both have 20% of the variance explained (see Figure S1 in the Supporting Information). So instead, co-occurrence patterns were analyzed using graph theory and a selection scheme based on Pearson product-moment correlation coefficient R . We set a condition for determining the presence of strong collinearity: for | R | > 0.5, two distinct variables are considered strongly collinear; otherwise, they are treated to be independent from each other.…”
Section: Computational Detailsmentioning
confidence: 99%
“…Network density, a fundamental measure in graph theory, was employed to assess the overall connectivity and complexity of hand movements during the laparoscopic simulation tasks. It is defined as the ratio of the number of actual edges in the network to the total possible number of edges [31]. A higher network density indicates a greater level of interconnectedness among participants' hand movements, suggesting increased coordination and information flow within the network.…”
Section: Network Densitymentioning
confidence: 99%
“…E is a set of edges that represent the logical communication channels between PEs. ψ is the graph mapping incident function ψ : E  V  V, which maps an edge onto a pair of vertices (vi,vj) [24].…”
Section: A a Graph-theoretic Approach For Application Modelingmentioning
confidence: 99%