p-means centrality

Andrade, Ricardo Lopes de; Rêgo, Leandro Chaves

doi:10.1016/j.cnsns.2018.08.002

Cited by 15 publications

(7 citation statements)

References 26 publications

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“…There are other kernels and transformations [9,48] that can also be used in a similar way. Say, every distance can be integrated in the p-Means framework [43] or in the framework of [3]. In [58], a Forest kernel closeness centrality (without square-rooting) has been examined.…”

Section: Centralities Based On Graph Kernelsmentioning

confidence: 99%

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How to choose the most appropriate centrality measure?

Chebotarev¹,

Gubanov²

2020

Preprint

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We propose a new method to select the most appropriate network centrality measure based on the user's opinion on how such a measure should work on a set of simple graphs. The method consists in: (1) forming a set F of candidate measures; (2) generating a sequence of sufficiently simple graphs that distinguish all measures in F on some pairs of nodes; (3) compiling a survey with questions on comparing the centrality of test nodes; (4) completing this survey, which provides a centrality measure consistent with all user responses. The developed algorithms make it possible to implement this approach for any finite set F of measures. This paper presents its realization for a set of 40 centrality measures. The proposed method called culling can be used for rapid analysis or combined with a normative approach by compiling a survey on the subset of measures that satisfy certain normative conditions (axioms). In the present study, the latter was done for the subsets determined by the Self-consistency or Bridge axioms.

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Section: Centralities Based On Graph Kernelsmentioning

confidence: 99%

“…2. In some cases, we have a parametric family of measures with a real parameter (see, e.g., [3,42,43]). Even if such a family has been characterized, the problem of choosing the value of the parameter still remains.…”

Section: Introduction and Related Workmentioning

confidence: 99%

How to choose the most appropriate centrality measure?

Chebotarev¹,

Gubanov²

2020

Preprint

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“…Wang et al [24] then suggested a more precise ranking of prominent nodes in a weighted neighborhood centrality. In this paper, we have introduced the new concept of centrality measure [22,[25][26][27][28][29] and established a real-life application by this proposed method. Samanta et al [30] introduced a new concept of centrality measure in a network.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Centrality Measurement Using Generalized Fuzzy Graphs

Dhanalakshmi¹,

Rashid

Kim

et al. 2022

Discrete Dynamics in Nature and Society

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The fuzzy graph is the foundation of many actual structures such as networking, picture planning, and so on. Generalized fuzzy graphs (GFG) are ideal for avoiding certain constraints of fuzzy graphs. Social networks are a useful and powerful way to link citizens worldwide. A central person in a social network is to deny the leading people in it. Different centrality steps have also been established over the years. We evaluated the centrality of the social network through a general fuzzy graph in this article. An application to detect the central person in any online group like WhatsApp is described in this study by using generalized fuzzy graphs. Also, a few important results are established in this study.

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“…For example, Mariani et al [35] proposed a local centrality measure named social capital can fast identify influencer which is based on the local network structure properties. Andrade et al [36] proposed p-means centrality based on the average of the geodesic distances, and obtained the greatest spreading capacity node in the network. Deng et al [37] identified the vital nodes by inverse-square law in the complex network.…”

Section: Introductionmentioning

confidence: 99%

Vital spreaders identification in complex networks with multi-local dimension

Wen

Pelusi

Deng

2020

Knowledge-Based Systems

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The important nodes identification has been an interesting problem in this issue. Several centrality measures have been proposed to solve this problem, but most of previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and q plays a weighting coefficient for this partition information. MLD would have different expressions with different value of q, and it would degenerate to local information dimension and variant of local dimension when q = 1 when q = 0 respectively, both of which have been effective identification method for influential nodes. Thus, MLD would be a more general method which can degenerate to some exiting centrality measures. In addition, different with classical methods, the node with low MLD would be more important in the network. Some comparison methods and real-world complex networks are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method.

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p-means centrality

Cited by 15 publications

References 26 publications

How to choose the most appropriate centrality measure?

How to choose the most appropriate centrality measure?

A New Method for Centrality Measurement Using Generalized Fuzzy Graphs

Vital spreaders identification in complex networks with multi-local dimension

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