Network robustness and residual closeness

Aytaç, Aysun; Berberler, Zeynep Nihan

doi:10.1051/ro/2016071

Cited by 20 publications

(16 citation statements)

References 29 publications

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“…More information on closeness, residual closeness, and additional closeness can be found in [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Previous Resultsmentioning

confidence: 99%

Closeness of Splitting Graphs

Dangalchev¹

2020

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“…More information on closeness, residual closeness, and additional closeness can be found in [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Previous Resultsmentioning

confidence: 99%

Closeness of Splitting Graphs

Dangalchev¹

2020

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“…Recall that there have been lots of results on the computational aspect [2][3][4]11,12,18] and on the extremal aspect [8,22,24]. The residual spectral closeness may be used as a spectral measure of graph or network structures.…”

Section: Residual Spectral Closenessmentioning

confidence: 99%

On the spectral closeness and residual spectral closeness of graphs

Zheng

Zhou

2022

RAIRO-Oper. Res.

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The spectral closeness of a graph $G$ is defined as the spectral radius of the closeness matrix of $G$, whose $(u,v)$-entry for vertex $u$ and vertex $v$ is $2^{-d_G(u,v)}$ if $u\not= v$ and $0$ otherwise, where $d_G(u,v)$ is the distance between $u$ and $v$ in $G$. The residual spectral closeness of $G$ is defined as the minimum spectral closeness of the subgraphs of $G$ with one vertex deleted. We propose local grafting operations that decrease or increase the spectral closeness and determine those graphs that uniquely minimize and/or maximize the spectral closeness in some families of graphs. We also discuss extremal properties of the residual spectral closeness.

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“…The robustness of a network is the ability to maintain the structural integrity and functional continuity of the system in case of failures of nodes or links [14][15][16]. Many scholars have researched the robustness of the OSC, providing practical and valuable insights.…”

Section: Introductionmentioning

confidence: 99%

Robustness of Multi-Project Knowledge Collaboration Network in Open Source Community

Zhang

Lei

Sun

et al. 2023

Entropy

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Multi-project parallelism is an important feature of open source communities (OSCs), and multi-project collaboration among users is a favorable condition for an OSC’s development. This paper studies the robustness of this type of community. Based on the characteristics of knowledge collaboration behavior and the large amount of semantic content generated from user collaboration in open source projects, we construct a directed, weighted, semantic-based multi-project knowledge collaboration network. Using analysis of the KCN’s structure and user attributes, nodes are divided into knowledge collaboration nodes and knowledge dissemination nodes that participate in either multi- or single-project collaboration. From the perspectives of user churn and behavior degradation, two types of failure modes are constructed: node failure and edge failure. Based on empirical data from the Local Motors open source vehicle design community, we then carry out a dynamic robustness analysis experiment. Our results show that the robustness of our constructed network varies for different failure modes and different node types: the network has (1) a high robustness to random failure and a low robustness to deliberate failure, (2) a high robustness to edge failure and a low robustness to node failure, and (3) a high robustness to the failure of single-project nodes (or their edges) and a low robustness to the failure of multi-project nodes (or their edges). These findings can be used to provide a more comprehensive and targeted management reference, promoting the efficient development of OSCs.

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Network robustness and residual closeness

Cited by 20 publications

References 29 publications

Closeness of Splitting Graphs

Closeness of Splitting Graphs

On the spectral closeness and residual spectral closeness of graphs

Robustness of Multi-Project Knowledge Collaboration Network in Open Source Community

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