Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition

We investigated competitive conditions in global value chains (GVCs) for a period of fifteen years (2000–2014), focusing on sector structure, countries’ dominance and diversification. For this purpose, we used data from the World Input–Output Database (WIOD) and examined GVCs as weighted directed networks, where countries are the nodes and value added flows are the edges. We compared the in-and out-weighted degree centralization of the sectoral GVC networks in order to detect the most centralized, on the import or export side, respectively (oligopsonies and oligopolies). Moreover, we examined the in- and out-weighted degree centrality and the in- and out-weight entropy in order to determine whether dominant countries are also diversified. The empirical results reveal that diversification (entropy) and dominance (degree) are not correlated. Dominant countries (rich) become more dominant (richer). Diversification is not conditioned by competitiveness.

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A New Weighted Degree Centrality Measure: The Application in an Animal Disease Epidemic

PLoS ONE ◽

10.1371/journal.pone.0165781 ◽

2016 ◽

Vol 11 (11) ◽

pp. e0165781 ◽

Cited By ~ 10

Author(s):

Luca Candeloro ◽

Lara Savini ◽

Annamaria Conte

Keyword(s):

Degree Centrality ◽

Animal Disease ◽

Weighted Degree ◽

Centrality Measure ◽

Weighted Degree Centrality

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Predicting Essential Proteins Based on Weighted Degree Centrality

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2013.2295318 ◽

2014 ◽

Vol 11 (2) ◽

pp. 407-418 ◽

Cited By ~ 96

Author(s):

Xiwei Tang ◽

Jianxin Wang ◽

Jiancheng Zhong ◽

Yi Pan

Keyword(s):

Degree Centrality ◽

Weighted Degree ◽

Essential Proteins ◽

Weighted Degree Centrality

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Improved influential nodes identification in complex networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202943 ◽

2021 ◽

pp. 1-9

Author(s):

Shi Dong ◽

Wengang Zhou

Keyword(s):

Theoretical Analysis ◽

Complex Networks ◽

Network Structure ◽

Information Entropy ◽

Network System ◽

Weighted Degree ◽

Identification Methods ◽

Hybrid Mechanism ◽

Influential Nodes ◽

Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Symmetry ◽

10.3390/sym13091570 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1570 ◽

Cited By ~ 1

Author(s):

Jingcheng Zhu ◽

Lunwen Wang

Keyword(s):

Complex Networks ◽

Sir Model ◽

Identification Accuracy ◽

Degree Centrality ◽

Network Stability ◽

Propagation Process ◽

Calculation Results ◽

Influential Nodes ◽

Nearest Neighborhood ◽

Accuracy Degree

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

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Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

International Journal of Modern Physics B ◽

10.1142/s0217979218501187 ◽

2018 ◽

Vol 32 (06) ◽

pp. 1850118 ◽

Cited By ~ 5

Author(s):

Mengtian Li ◽

Ruisheng Zhang ◽

Rongjing Hu ◽

Fan Yang ◽

Yabing Yao ◽

...

Keyword(s):

Complex Networks ◽

Nearest Neighbors ◽

Sir Model ◽

Clustering Coefficient ◽

Degree Centrality ◽

Competitive Performance ◽

Spreading Process ◽

Degree Sum ◽

Local Degree ◽

Crucial Problem

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

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Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Complexity ◽

10.1155/2020/7195494 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Xiaole Wan ◽

Zhen Zhang ◽

Chi Zhang ◽

Qingchun Meng

Keyword(s):

Stock Market ◽

Complex Networks ◽

Robustness Analysis ◽

Risk Identification ◽

Network Robustness ◽

Degree Centrality ◽

Market Risks ◽

Stationary Test ◽

Systematic Risks ◽

Quantitative Indexes

The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

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Efficiency centrality in time-varying graphs

Acta Universitatis Sapientiae Informatica ◽

10.2478/ausi-2020-0001 ◽

2020 ◽

Vol 12 (1) ◽

pp. 5-21

Author(s):

Péter Marjai ◽

Attila Kiss

Keyword(s):

Complex Networks ◽

Betweenness Centrality ◽

Cascade Model ◽

Closeness Centrality ◽

Degree Centrality ◽

Time Varying ◽

Independent Cascade Model ◽

Independent Cascade ◽

Influential Nodes ◽

Time Varying Graphs

AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

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Towards a Weighted Version of the Hajnal–Szemerédi Theorem

Combinatorics Probability Computing ◽

10.1017/s0963548313000059 ◽

2013 ◽

Vol 22 (3) ◽

pp. 346-350 ◽

Cited By ~ 1

Author(s):

JOZSEF BALOGH ◽

GRAEME KEMKES ◽

CHOONGBUM LEE ◽

STEPHEN J. YOUNG

Keyword(s):

Positive Integer ◽

Minimum Degree ◽

Edge Weight ◽

Main Question ◽

Weighted Graphs ◽

Lower And Upper Bounds ◽

Weighted Degree ◽

Weighted Version ◽

Vertex Disjoint ◽

The Given

For a positive integer r ≥ 2, a Kr-factor of a graph is a collection vertex-disjoint copies of Kr which covers all the vertices of the given graph. The celebrated theorem of Hajnal and Szemerédi asserts that every graph on n vertices with minimum degree at least $(1-\frac{1}{r})n contains a Kr-factor. In this note, we propose investigating the relation between minimum degree and existence of perfect Kr-packing for edge-weighted graphs. The main question we study is the following. Suppose that a positive integer r ≥ 2 and a real t ∈ [0, 1] is given. What is the minimum weighted degree of Kn that guarantees the existence of a Kr-factor such that every factor has total edge weight at least $$t\binom{r}{2}$?$ We provide some lower and upper bounds and make a conjecture on the asymptotics of the threshold as n goes to infinity.

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Effect of rich-club on diffusion in complex networks

International Journal of Modern Physics B ◽

10.1142/s0217979218501424 ◽

2018 ◽

Vol 32 (12) ◽

pp. 1850142 ◽

Cited By ~ 7

Author(s):

Kamal Berahmand ◽

Negin Samadi ◽

Seyed Mahmood Sheikholeslami

Keyword(s):

Complex Networks ◽

Time Complexity ◽

Linear Time ◽

Local Information ◽

Degree Centrality ◽

Rich Club ◽

Influential Nodes ◽

The Rich ◽

Artificial Datasets

One of the main issues in complex networks is the phenomenon of diffusion in which the goal is to find the nodes with the highest diffusing power. In diffusion, there is always a conflict between accuracy and efficiency time complexity; therefore, most of the recent studies have focused on finding new centralities to solve this problem and have offered new ones, but our approach is different. Using one of the complex networks’ features, namely the “rich-club”, its effect on diffusion in complex networks has been analyzed and it is demonstrated that in datasets which have a high rich-club, it is better to use the degree centrality for finding influential nodes because it has a linear time complexity and uses the local information; however, this rule does not apply to datasets which have a low rich-club. Next, real and artificial datasets with the high rich-club have been used in which degree centrality has been compared to famous centrality using the SIR standard.

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