‘Clumpiness’ mixing in complex networks

Estrada, Ernesto; Hatano, Naomichi; Gutierrez, Amauri

doi:10.1088/1742-5468/2008/03/p03008

Cited by 12 publications

(14 citation statements)

References 45 publications

(84 reference statements)

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“…Some generalizations and statistical-mechanics interpretation of the clumpiness coefficient have been recently published by Estrada et al (2008). Some generalizations and statistical-mechanics interpretation of the clumpiness coefficient have been recently published by Estrada et al (2008).…”

Section: Clumpiness Coefficientmentioning

confidence: 99%

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Using Network Centrality Measures to Manage Landscape Connectivity

Estrada

Bodin

2008

Ecological Applications

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230

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We use a graph-theoretical landscape modeling approach to investigate how to identify central patches in the landscape as well as how these central patches influence (1) organism movement within the local neighborhood and (2) the dispersal of organisms beyond the local neighborhood. Organism movements were theoretically estimated based on the spatial configuration of the habitat patches in the studied landscape. We find that centrality depends on the way the graph-theoretical model of habitat patches is constructed, although even the simplest network representation, not taking strength and directionality of potential organisms flows into account, still provides a coarse-grained assessment of the most important patches according to their contribution to landscape connectivity. Moreover, we identify (at least) two general classes of centrality. One accounts for the local flow of organisms in the neighborhood of a patch, and the other accounts for the ability to maintain connectivity beyond the scale of the local neighborhood. Finally, we study how habitat patches with high scores on different network centrality measures are distributed in a fragmented agricultural landscape in Madagascar. Results show that patches with high degree and betweenness centrality are widely spread, while patches with high subgraph and closeness centrality are clumped together in dense clusters. This finding may enable multispecies analyses of single-species network models.

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Section: Clumpiness Coefficientmentioning

confidence: 99%

“…where > stands for the transpose of the vector. Some generalizations and statistical-mechanics interpretation of the clumpiness coefficient have been recently published by Estrada et al (2008).…”

Section: Clumpiness Coefficientmentioning

confidence: 99%

Using Network Centrality Measures to Manage Landscape Connectivity

Estrada

Bodin

2008

Ecological Applications

Self Cite

230

183

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“…Central amino acids do not tend to associate with each other, thus protein structure networks are not 'clumpy' ( Fig. 2A; [Estrada et al, 2008]). …”

Section: Types Of Disorder In Network Topologymentioning

confidence: 99%

Disordered Proteins and Network Disorder in Network Descriptions of Protein Structure, Dynamics and Function: Hypotheses and a Comprehensive Review

Csermely¹,

Sandhu²,

Hazai³

et al. 2012

CPPS

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During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into 'cumulus-type', i.e., those similar to puffy (white) clouds, and 'stratus-type', i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an 'energy transfer' mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by 'multi-trajectories'; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach 'rarely visited' but functionally-related states. We also show the role of disorder in 'spatial games' of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks.

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“…Clumpiness matrix(Ξ) was initially introduced by Estrada et al [6] to extract parameters for classifying complex networks. For a network with n nodes this is an n × n matrix, with elements…”

Section: Clumpiness Matrixmentioning

confidence: 99%

“…In this paper we use the recently introduced "clumpiness matrix" of a network [6] to identify its community structure. The clumpiness measure was introduced to give more information about the structure of networks and can be used as a measure to classify them.…”

Section: Introductionmentioning

confidence: 99%

Community detection based on the “clumpiness” matrix in complex networks

Faqeeh

Samani

2012

Physica A: Statistical Mechanics and its Applications

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The "clumpiness" matrix of a network is used to develop a method to identify its community structure. A "projection space" is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or hierarchical clustering methods. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.

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‘Clumpiness’ mixing in complex networks

Cited by 12 publications

References 45 publications

Using Network Centrality Measures to Manage Landscape Connectivity

Using Network Centrality Measures to Manage Landscape Connectivity

Disordered Proteins and Network Disorder in Network Descriptions of Protein Structure, Dynamics and Function: Hypotheses and a Comprehensive Review

Community detection based on the “clumpiness” matrix in complex networks

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