<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1482" altimg="si50.svg"><mml:mi>k</mml:mi></mml:math>-core: Theories and applications

Kong, Yi-Xiu; Shi, Gui-Yuan; Wu, Ranchao; Zhang, Yicheng

doi:10.1016/j.physrep.2019.10.004

Cited by 124 publications

(53 citation statements)

References 92 publications

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“…A -shell is defined as the set of nodes belonging to the k th core but not to the th core 15 . The -core decomposition has proven to be useful in a variety of domains such as identifying and ranking the most influential spreaders in networks, identifying keywords used for classifying documents, and in assessing the robustness of mutualistic ecosystem and protein networks 16 , 17 .…”

Section: Introductionmentioning

confidence: 99%

Interplay between $$k$$-core and community structure in complex networks

Malvestio

Cardillo

Masuda

2020

Sci Rep

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The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as $$k$$ k -core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost $$k$$ k -shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the $$k$$ k -core decomposition of many empirical networks cannot be explained by the degree of each node alone, or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the $$k$$ k -core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the $$k$$ k -shell structure of the networks can be accounted for by the community structure. We find that preserving the community structure in the randomisation process is crucial for generating networks whose $$k$$ k -core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost $$k$$ k -shells into a small number of communities.

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Section: Introductionmentioning

confidence: 99%

Interplay between $$k$$-core and community structure in complex networks

Malvestio

Cardillo

Masuda

2020

Sci Rep

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“…For extensive review of network measures and influential node identification methods, the reader is referred to the following comprehensive review and research articles. 23 , 24 , 25 , 26 , 27 , 28 …”

Section: Introductionmentioning

confidence: 99%

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

2020

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Summary Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their applicability. To overcome this computational deficit, we undertook a statistical assessment of 200 real-world and simulated networks to decipher associations between centrality measures and developed an algorithm termed Integrated Value of Influence (IVI), which integrates the most important and commonly used network centrality measures in an unbiased way. When compared against 12 other contemporary influential node identification methods on ten different networks, the IVI algorithm outperformed all other assessed methods. Using this versatile method, network researchers can now identify the most influential network nodes.

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“…Previous studies (Xiao et al 2016;Kadi et al 2017;Zhao et al 2014) generally set thresholds to screen keywords according to the word frequency or edge weights, but these methods did not consider the possible effect of semantic association between two keywords. Seidman (1983) proposed the K-core approach to express the specific hierarchical structure properties and hierarchical characteristics of networks, and this method has been widely applied to hierarchical decomposition networks (Zhang et al 2008; Kong et al 2019;Kitsak et al 2010;Orman et al 2009). Notably, the K-core approach can be used to decompose core co-occurrence relationships and can be combined with the Louvain algorithm to efficiently detect the community structure and explore the subject-level and fine-scale information related to landslide monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Discovery Method for Landslide Monitoring Based on K-core Decomposition and the Louvain Algorithm

Wang¹,

Zhu²,

Li³

et al. 2021

Preprint

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Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of keyword co-occurrence network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines the K-core decomposition and Louvain algorithms. In this method, author keywords from the literature are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined for K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. In the resulting 5-core network subgraph of landslide monitoring keyword co-occurrence, 17 community structures can be identified, and the degree value and density of subcommunities are analysed by taking the community with the largest proportion of nodes as an example. The results show that the retention time of the proposed method is significantly lower than that of the traditional method.

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k-core: Theories and applications

Cited by 124 publications

References 92 publications

Interplay between $$k$$-core and community structure in complex networks

Interplay between $$k$$-core and community structure in complex networks

Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

A Knowledge Discovery Method for Landslide Monitoring Based on K-core Decomposition and the Louvain Algorithm

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