Identifying influential spreaders by weight degree centrality in complex networks

Liu, Yang; Wei, Bo; Du, Yuxian; Xiao, Fuyuan; Deng, Yong

doi:10.1016/j.chaos.2016.01.030

Cited by 48 publications

(27 citation statements)

References 47 publications

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“…In the following experiments, the KLD index will be compared with six evaluation indices, such as the clustering coefficient [35] (labeled as the C index), the extended neighborhood coreness centrality [15] (labeled as the C nc+ index), the weight degree centrality method (labeled as the W DC index), the extended weight degree centrality method [12] (labeled as the EW DC index), the extended gravity method [16] (labeled as the G + index), and the semi-local centrality method [11] (labeled as the SL index). Here, we briefly review the definitions of the six indices that will be discussed in this work in Table 2.…”

Section: Contrast Indicesmentioning

confidence: 99%

“…It achieves a good balance between accuracy and time complexity of the algorithm. Liu et al [12] proposed a weight degree centrality method that consists of the degree of a node and its neighborhoods, and this method worked well in most experiments. Kitsak et al [13] believed that the node importance was related to the location of the network, and they proposed a k-shell decomposition method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Node Importance Ranking of Complex Network based on Degree and Network Density

Xu¹,

Zhang²,

Yang³

et al. 2019

IJPE

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Node importance ranking of complex networks is of great significance to the study of network robustness. The classical centrality measure degree can reflect the number of neighbors of a node, but it ignores the information between its neighbors. In order to mine the important nodes in the network accurately and efficiently, a method of ranking the node importance of complex networks based on multiattribute evaluation and node deletion is proposed in this paper. Based on the degree attributes of the target node and its neighbors, this method introduces two attributes, which are the local network density centered on the target node and the assortativity coefficient. It takes into account the characteristics of the scale, tightness, and topology of the local area network where the node and its neighbors are located. This paper conducts deliberate attack experiments on four real networks. Through a comparison between the experimental results of the maximal connected coefficient and network efficiency, our approach is proven to be valid and feasible.

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Section: Contrast Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Node Importance Ranking of Complex Network based on Degree and Network Density

Xu¹,

Zhang²,

Yang³

et al. 2019

IJPE

View full text Add to dashboard Cite

show abstract

“…Many methods have been proposed to measure influence of nodes in the network, such as degree, betweenness, closeness and so on. As we know, degree [20], closeness [1], betweenness [24] and pagerank [14] are proposed to represent node influence in the network. However, many methods only consider node local attributes and ignores that nodes are also related to global properties of the network.…”

Section: A Influence Of Nodesmentioning

confidence: 99%

$\mathcal{K}$ -Lowest-Influence Overlapping Nodes Based Community Detection in Complex Networks

Tang

Liu

et al. 2019

IEEE Access

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Community detection is a classic and essential task in complex network analysis which aims at understanding their structural properties and functional organization. Community detection based on overlapping nodes has become one of the most popular methods in recent years. But, how to define the overlapping nodes in the real network is an important job. In this paper, we use the speaker-listener label propagation algorithm to find overlapping nodes. Second, we proposed a new metric which is based on local and global attributes to measure the node influence so as to evaluate the overlapping nodes' importance. Community detection is realized with the k-lowest-influence overlapping nodes deleting. Then, we assign the removed nodes into specific communities by voting to find the final community structure. The voting strategy is based on choosing the specific communities containing most of their neighbors. Finally, the extensive experiments on real-world networks demonstrate that our proposed method improves the quality of community detection methods and show both the effectiveness and efficiency of the method.

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“…The neighborhood coreness is labeled as + ( ) and + ( ) denotes the extended neighborhood coreness, which is the second-order neighborhood coreness. Liu et al [36] proposed a weight degree centrality (labeled as ) to measure the influence of node propagation and regulate the weight between the degree and the ability of spreading out with a tuning parameter . And the extended weight degree centrality method is labeled as .…”

Section: Contrast Centrality Indicesmentioning

confidence: 99%

Identifying Important Nodes in Complex Networks Based on Multiattribute Evaluation

Zhang

Yang

et al. 2018

Mathematical Problems in Engineering

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Assessing and measuring the importance of nodes in a complex network are of great theoretical and practical significance to improve the robustness of the actual system and to design an efficient system structure. The classical local centrality measures of important nodes only take the number of node neighbors into consideration but ignore the topological relations and interactions among neighbors. Due to the complexity of the algorithm itself, the global centrality measure cannot be applied to the analysis of large-scale complex network. The k-shell decomposition method considers the core node located in the center of the network as the most important node, but it only considers the residual degree and neglects the interaction and topological structure between the node and its neighbors. In order to identify the important nodes efficiently and accurately in the network, this paper proposes a local centrality measurement method based on the topological structure and interaction characteristics of the nodes and their neighbors. On the basis of the k-shell decomposition method, the method we proposed introduces two properties of structure hole and degree centrality, which synthetically considers the nodes and their neighbors’ network location information, topological structure, scale characteristics, and the interaction between different nuclear layers of them. In this paper, selective attacks on four real networks are, respectively, carried out. We make comparative analyses of the averagely descending ratio of network efficiency between our approach and other seven indices. The experimental results show that our approach is valid and feasible.

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Identifying influential spreaders by weight degree centrality in complex networks

Cited by 48 publications

References 47 publications

Node Importance Ranking of Complex Network based on Degree and Network Density

Node Importance Ranking of Complex Network based on Degree and Network Density

$\mathcal{K}$ -Lowest-Influence Overlapping Nodes Based Community Detection in Complex Networks

Identifying Important Nodes in Complex Networks Based on Multiattribute Evaluation

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