Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition

García-Algarra, Javier; Pastor, Juan Manuel; Iriondo, José María; Galeano, Javier

doi:10.7287/peerj.preprints.2855

Cited by 5 publications

(5 citation statements)

References 3 publications

(3 reference statements)

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“…In nested hierarchy higher-level elements are contained in lower-level elements while in flow hierarchy nodes are arranged in different levels such that influential nodes are at a higher level and they are connected to the nodes they influence. Main nested hierarchy measures (k-core and k-truss) are based on hierarchical decomposition of nodes [19]- [21]. To our knowledge, Local Reaching Centrality" (LRC) is the only flow hierarchy measure used to quantify node hierarchy [14].…”

Section: Introductionmentioning

confidence: 99%

Interplay Between Hierarchy and Centrality in Complex Networks

et al. 2020

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Hierarchy and centrality are two popular notions used to characterize the importance of entities in complex systems. Indeed, many complex systems exhibit a natural hierarchical structure, and centrality is a fundamental characteristic allowing to identify key constituents. Several measures based on various aspects of network topology have been proposed in order to quantify these concepts. While numerous studies have investigated whether centrality measures convey redundant information, how centrality and hierarchy measures are related is still an open issue. In this paper, we investigate the association between centrality and hierarchy using several correlation and similarity evaluation measures. A series of experiments is performed in order to evaluate the combinations of 6 centrality measures with 4 hierarchy measures across 28 diverse real-world networks with varying topological characteristics. Results show that network density and transitivity play a key role in shaping the redundancy between centrality and hierarchy measures.

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Section: Introductionmentioning

confidence: 99%

Interplay Between Hierarchy and Centrality in Complex Networks

et al. 2020

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“…Although we obtained the PPI network of these 109 proteins, their underlying connections cannot be directly reflected by this network structure. Therefore, on the basis of GENE MANIA analysis, we selected 82 proteins with score above 0.6 for k-core analysis in Cytoscape3.7.1 using MCODE.K-core is a topological analysis that decomposes the network relation of interaction, and find out the important nodes in the complex network relation structure (25).In this study, we set the parameters of degree>15 and k-core=2 to identify key nodes in the co-expression network (Fig.7). This sub network containsHspb1、 Dnmt1、 Mmp2、 Thbs1、 Crebbp、 Hmgb1、Acta2、Cdkn1b、Atg7、Tsc2 and Icam1.…”

Section: Co-expression and Topology Analysis Of Over Medium-confidencementioning

confidence: 99%

High Throughput Bioinformatics-based Prediction of the targets for BushenjieduDecoction in preventing relapse of acute leukemia

Sun

Yang

Shi

et al. 2020

Preprint

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Leukemia is a lethal myeloproliferative disorder, its’ relapse following chemotherapy is the major concern in clinical practice. For a long time, we found that traditional Chinese medicines such as Bushenjiedudecoction (BSJD) have significant effects on delaying relapse. However, the underlying mechanisms are not clear, which limits the clinical application of BSJD decoction. Therefore, we tried to make some explorations in this study. We isolated mesenchymal stem cells (MSC) after treated them with BSJD for proteomic analysis. And then 109 targets were screened out through analysis of the shared proteins of that affected by BSJD and those related to leukemia. Subsequently, the data were analyzed by GO functions, KEGG pathways, PPI network and topological analysis, and then some nodes were selected for animal experiment. As a result, we demonstrated the effective targets of BSJD on MSC through bioinformatics analysis and explored the potential mechanism of BSJD from its influence on niches.

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“…For example, in [57] an application is presented to plot bipartite ecological networks. Also, in [58], the authors study different techniques to identifying the species for which the networks are most vulnerable to cascade extinctions. It turns out that the core decomposition concept sheds light on the understanding of the robustness properties in mutualistic networks.…”

Section: Physics Biology and Ecologymentioning

confidence: 99%

The core decomposition of networks: theory, algorithms and applications

et al. 2019

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The core decomposition of networks has attracted significant attention due to its numerous applications in real-life problems. Simply stated, the core decomposition of a network (graph) assigns to each graph node v, an integer number c(v) (the core number), capturing how well v is connected with respect to its neighbors. This concept is strongly related to the concept of graph degeneracy, which has a long history in Graph Theory. Although the core decomposition concept is extremely simple, there is an enormous interest in the topic from diverse application domains, mainly because it can be used to analyze a network in a simple and concise manner by quantifying the significance of graph nodes. Therefore, there exists a respectable number of research works that either propose efficient algorithmic techniques under different settings and graph types or apply the concept to another problem or scientific area. Based on this large interest in the topic, in this survey, we perform an in-depth discussion of core decomposition, focusing mainly on: i) the basic theory and fundamental concepts, ii) the algorithmic techniques proposed for computing it efficiently under different settings, and iii) the applications that can benefit significantly from it.

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Ranking of critical species to preserve the functionality of mutualistic networks using the k-core decomposition

Cited by 5 publications

References 3 publications

Interplay Between Hierarchy and Centrality in Complex Networks

Interplay Between Hierarchy and Centrality in Complex Networks

High Throughput Bioinformatics-based Prediction of the targets for BushenjieduDecoction in preventing relapse of acute leukemia

The core decomposition of networks: theory, algorithms and applications

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