A new closeness centrality measure via effective distance in complex networks

Du, Yuxian; Gao, Cai; Chen, Xin; Hu, Yong; Sadiq, Rehan; Deng, Yong

doi:10.1063/1.4916215

Cited by 74 publications

(45 citation statements)

References 45 publications

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“…In addition, ϕ i represents the set of neighboring nodes with the shortest path length less than the specified length (i.e., d ij ≤ r, without loss of generality, we set r = 3 since the network size is so large that the computing cost is very high if r > 3), and node j is an element of ϕ i . In order to further compare the proposed method with the classical measures, we use the simple network as examples [26][27][28]. In Figure. 1, we show the node schematic diagram of a random network, and evaluate the importance of the nodes in this network through several classical measures and the proposed method.…”

Section: Methodsmentioning

confidence: 99%

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

Dong

2019

Int. J. Mod. Phys. B

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The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequences. In this paper, we proposed the clustering H-index mixing (CHM) centrality based on the Hindex of the node itself and the relative distance of its neighbors. Starting from the node itself and combining with the topology around the node, the importance of the node and its spreading capability were determined. In order to evaluate the performance of the proposed method, we use Susceptible-Infected-Recovered (SIR) model, monotonicity and resolution as the evaluation standard of experiment. Experimental results in artificial networks and real-world networks show that CHM centrality has excellent performance in identifying node importance and its spreading capability.

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Section: Methodsmentioning

confidence: 99%

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

Dong

2019

Int. J. Mod. Phys. B

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“…Closeness centrality was as an index of the score of the node in the network (Du et al, 2015). The intimacy coefficient was also called tightness, and these values indicated the distance of genes to the centre of the network.…”

Section: Protein-protein Interaction (Ppi) Networkmentioning

confidence: 99%

Comparative transcriptomic analysis reveals beneficial effect of dietary mulberry leaves on the muscle quality of finishing pigs

Chen

Cai

et al. 2019

Veterinary Medicine & Sci

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BackgroundThe aim of this study was to investigate the effect of dietary mulberry leaves on the transcriptome profiles of finishing pigs. RNA‐Seq was used to identify differentially expressed genes (DEGs) in the longissimus dorsi of 56 pigs fed either a traditional diet or diets supplemented with 3%, 6% or 9% mulberry leaf powder, and both gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed. Furthermore, protein–protein interaction (PPI) network and the subnet module analysis were used to identify genes with beneficial potential, and quantitative real‐time polymerase chain reaction (qRT‐PCR) was used to validate the expression patterns revealed by RNA‐Seq.ResultsPigs fed with the 6% mulberry diet exhibited greater average daily gain, lower water loss and lower shear force than the control group and yielded 531 DEGs, including 271 and 260 upregulated and downregulated genes, respectively. Function analysis revealed that the DEGs were significantly enriched in functions related to muscle growth and development. Furthermore, several genes (i.e. ACOT4, ECHS1, HACD1, NPR1, ADCY2, MGLL and IRS1) were enriched in a KEGG pathway that was associated with fatty acid metabolism, and in the PPI subnet module, four of eight node genes, namely TNNC1, MYL3, TCAP and TNNT1, were associated with muscle formation and development. The upregulation of these genes, including TNNC1, TNNT1 and MYL3, was confirmed by qRT‐PCR.ConclusionsDietary mulberry leaves (6%) may improve the muscle quality of pigs by modulating the expression of several key genes, such as TNNC1, MYL3 and TNNT1.

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“…But in other types of networks like weighted networks, directed networks, the link weights and directions also affects the distance between two nodes. So, the closeness centrality has been extended to these networks like weighted networks [3], directed networks [4], disconnected networks [5], multilayer networks [6], overlapped community structure [7], and so on.…”

Section: Related Workmentioning

confidence: 99%

Fast Estimation of Closeness Centrality Ranking

Saxena

Gera

Iyengar

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Closeness centrality is one way of measuring how central a node is in the given network. The closeness centrality measure assigns a centrality value to each node based on its accessibility to the whole network. In real life applications, we are mainly interested in ranking nodes based on their centrality values. The classical method to compute the rank of a node first computes the closeness centrality of all nodes and then compares them to get its rank. Its time complexity is O(n · m + n), where n represents total number of nodes, and m represents total number of edges in the network. In the present work, we propose a heuristic method to fast estimate the closeness rank of a node in O(α · m) time complexity, where α = 3. We also propose an extended improved method using uniform sampling technique. This method better estimates the rank and it has the time complexity O(α · m), where α ≈ 10 − 100. This is an excellent improvement over the classical centrality ranking method. The efficiency of the 1 proposed methods is verified on real world scale-free social networks using absolute and weighted error functions.

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A new closeness centrality measure via effective distance in complex networks

Cited by 74 publications

References 45 publications

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

Comparative transcriptomic analysis reveals beneficial effect of dietary mulberry leaves on the muscle quality of finishing pigs

Fast Estimation of Closeness Centrality Ranking

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