A Fusion Method for Node Importance Measurement in Complex Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

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Identifying node importance based on information entropy in complex networks

Physica Scripta ◽

10.1088/0031-8949/88/06/065201 ◽

2013 ◽

Vol 88 (6) ◽

pp. 065201 ◽

Cited By ~ 6

Author(s):

Fan Wenli ◽

Liu Zhigang ◽

Hu Ping

Keyword(s):

Complex Networks ◽

Information Entropy ◽

Node Importance

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Node Importance Evaluation of Anti-Missile Networks

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.765-767.1098 ◽

2013 ◽

Vol 765-767 ◽

pp. 1098-1102

Author(s):

Yu Xia ◽

Fei Peng

Keyword(s):

Complex Networks ◽

Complex Network ◽

Evaluation Method ◽

Simulation Experiments ◽

Node Importance ◽

Correlation Degree ◽

Evaluation Algorithm

in order to improve the efficiency and validity of node importance evaluation, a new evaluation method for node importance in complex networks was proposed based on node approach degree and node correlation degree. The basic idea of the method is that the larger the approach degree of a node is, the closer to center of a complex network the node is and the more important it is; the bigger the correlation degree of a node is, the more important the node is. An evaluation algorithm corresponding to the method was designed for the warship fleet cooperation anti-missile network. Finally, the validity of the proposed method was verified by simulation experiments.

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Integrated evaluation approach for node importance of complex networks based on relative entropy

Journal of Systems Engineering and Electronics ◽

10.21629/jsee.2016.06.10 ◽

2016 ◽

Vol 27 (6) ◽

pp. 1219-1226 ◽

Cited By ~ 4

Author(s):

Bin Chen ◽

◽

Zhixue Wang ◽

Chen Luo ◽

◽

...

Keyword(s):

Complex Networks ◽

Relative Entropy ◽

Evaluation Approach ◽

Node Importance

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Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

Mathematical Problems in Engineering ◽

10.1155/2019/9728742 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Yuanzhi Yang ◽

Lei Yu ◽

Zhongliang Zhou ◽

You Chen ◽

Tian Kou

Keyword(s):

Decision Making ◽

Complex Networks ◽

Multicriteria Decision Making ◽

Practical Significance ◽

Network Stability ◽

Weighting Method ◽

Multicriteria Decision ◽

Node Importance ◽

Subjective Factor ◽

Importance Ranking

Measuring node importance in complex networks has great theoretical and practical significance for network stability and robustness. A variety of network centrality criteria have been presented to address this problem, but each of them focuses only on certain aspects and results in loss of information. Therefore, this paper proposes a relatively comprehensive and effective method to evaluate node importance in complex networks using a multicriteria decision-making method. This method not only takes into account degree centrality, closeness centrality, and betweenness centrality, but also uses an entropy weighting method to calculate the weight of each criterion, which can overcome the influence of the subjective factor. To illustrate the effectiveness and feasibility of the proposed method, four experiments were conducted to rank node importance on four real networks. The experimental results showed that the proposed method can rank node importance more comprehensively and accurately than a single centrality criterion.

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Evaluation method for node importance in complex networks based on eccentricity of node

2016 2nd IEEE International Conference on Computer and Communications (ICCC) ◽

10.1109/compcomm.2016.7925149 ◽

2016 ◽

Author(s):

Qin Qiong ◽

Wang Dongxia

Keyword(s):

Complex Networks ◽

Evaluation Method ◽

Node Importance

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A New Measure of Node Importance in Complex Networks with Tunable Parameters

2008 4th International Conference on Wireless Communications, Networking and Mobile Computing ◽

10.1109/wicom.2008.1170 ◽

2008 ◽

Cited By ~ 8

Author(s):

Jianwei Wang ◽

Lili Rong ◽

Tianzhu Guo

Keyword(s):

Complex Networks ◽

Node Importance

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Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

Modern Physics Letters B ◽

10.1142/s0217984914502686 ◽

2015 ◽

Vol 29 (03) ◽

pp. 1450268 ◽

Cited By ~ 13

Author(s):

Fang Hu ◽

Yuhua Liu

Keyword(s):

Discriminant Analysis ◽

Complex Networks ◽

Linear Discriminant Analysis ◽

Eigenvector Centrality ◽

Complex Situation ◽

Linear Discriminant ◽

Multi Index ◽

Node Importance ◽

Evaluation Algorithm ◽

Important Nodes

The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

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