ArticleScientific reports2020
Estimating degree-degree correlation and network cores from the connectivity of high-degree nodes in complex networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Alzheimer's-specific brain amyloid interactome: Neural-network analysis of intra-aggregate crosslinking identifies novel drug targets.iScience · 2024Article
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- Identification of hot regions in hub protein-protein interactions by clustering and PPRA optimization.BMC medical informatics and decision making · 2021Article
- A novel diagnostic method based on filter bank theory for fast and accurate detection of thermoacoustic instability.Scientific reports · 2021Article
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Authors and funding
1 author.
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Abstract
Many of the structural characteristics of a network depend on the connectivity with and within the hubs. These dependencies can be related to the degree of a node and the number of links that a node shares with nodes of higher degree. In here we revise and present new results showing how to construct network ensembles which give a good approximation to the degree-degree correlations, and hence to the projections of this correlation like the assortativity coefficient or the average neighbours degree. We present a new bound for the structural cut-off degree based on the connectivity within the hubs. Also we show that the connections with and within the hubs can be used to define different networks cores. Two of these cores are related to the spectral properties and walks of length one and two which contain at least on hub node, and they are related to the eigenvector centrality. We introduce a new centrality measured based on the connectivity with the hubs. In addition, as the ensembles and cores are related by the connectivity of the hubs, we show several examples how changes in the hubs linkage effects the degree-degree correlations and core properties.
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Registered trials
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