Eccentricity and centrality in networks

Hage, Per; Harary, Frank

doi:10.1016/0378-8733(94)00248-9

Cited by 384 publications

(216 citation statements)

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“…"Eccentricity" describes how easily accessible a gene is from other genes (Hage and Harary, 1995), which is defined as: ECCðiÞ ¼ 1 maxfdistði; jÞg ; j∈V; jÞi. "Eigenvector" measures the importance of the nodes in the whole network, especially favoring the nodes that are connected to important neighbors (Bonacich, 2007).…”

Section: Computation Of Gene Network Characteristicsmentioning

confidence: 99%

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

Xin

Feldmann

et al. 2014

Plant Cell

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ORCID IDs: 0000-0001-9612-7898 (C.M.); 0000-0002-3306-5594 (M.X.); 0000-0002-6406-5597 (X.W.)Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning-based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stressresponsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing-based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress-related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.

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Section: Computation Of Gene Network Characteristicsmentioning

confidence: 99%

Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis

Xin

Feldmann

et al. 2014

Plant Cell

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“…The eccentricity of a node is the longest shortest-path distance between it and every other node, representing how far it is from the node furthest from it (Urban and Keitt 2001). A network's diameter is its greatest eccentricity and its radius is its smallest eccentricity (Hage and Harary 1995). A network's center is the nodes with an eccentricity equal to the radius.…”

Section: Structural Measures: Networkmentioning

confidence: 99%

Measuring the Complexity of Urban Form and Design

Boeing

2017

SSRN Journal

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Complex systems have become a popular lens for conceptualizing cities, and complexity has substantial implications for urban performance and resilience. This paper develops a typology of methods and measures for assessing the complexity of the built form at the scale of urban design. It extends quantitative methods from urban planning, network science, ecosystems studies, fractal geometry, and information theory to the physical urban form and the analysis of qualitative human experience. Metrics at multiple scales are scattered throughout these bodies of literature and have useful applications in analyzing the complexity that both evolves and results from local planning and design processes. The typology developed here applies to empirical research of multiple neighborhood types and design standards and it includes temporal, visual, spatial, fractal, and network-analytic measures of the urban form.

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“…However, there are several variants of centrality measures that could be taken into account. These variants include closeness centrality [22], graph centrality [9], and stress centrality [23]. All these concepts are attempts to capture the notion of the relative importance of a vertex in the overall structure of a graph, and thus each of them could play an important role in estimating the orbits of a graph.…”

Section: Definition 13 the Betweenness Centrality Of A Vertex X Of Amentioning

confidence: 99%