scholarly journals Node Importance Ranking of Complex Networks with Entropy Variation

Entropy ◽  
2017 ◽  
Vol 19 (7) ◽  
pp. 303 ◽  
Author(s):  
Xinbo Ai
Keyword(s):  
Complex Networks ◽  
Node Importance ◽  
Entropy Variation ◽  
Importance Ranking

scholarly journals Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
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.

scholarly journals Node importance ranking of complex networks

2013 ◽  
Vol 62 (17) ◽  
pp. 178901
Author(s):  
Liu Jian-Guo ◽  
Ren Zhuo-Ming ◽  
Guo Qiang ◽  
Wang Bing-Hong
Keyword(s):  
Complex Networks ◽  
Node Importance ◽  
Importance Ranking

scholarly journals Complex Network Filtering and Compression Algorithm Based on Triangle-Subgraph

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shuxia Ren ◽  
Tao Wu ◽  
Shubo Zhang
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Sir Model ◽  
Compression Algorithm ◽  
Sorting Algorithm ◽  
Ranking Algorithm ◽  
Original Network ◽  
Node Importance ◽  
Importance Ranking ◽  
Result Analysis

Compressing the data of a complex network is important for visualization. Based on the triangle-subgraph structure in complex networks, complex network filtering compression algorithm based on the triangle-subgraph is proposed. The algorithm starts from the edge, lists nodes of the edge and their common node sets to form a triangle-subgraph set, parses the triangle-subgraph set, and constructs new complex network to complete compression. Before calculating the set of triangle-subgraph, node importance ranking algorithm is proposed to extract high- and low-importance nodes and filter them to reduce computational scale of complex networks. Experimental results show that filtering compression algorithm can not only improve the compression rate but also retain information of the original network at the same time; sorting result analysis and SIR model analysis show that the sorting result of node importance sorting algorithm has accuracy and rationality.

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale 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.

Identifying node importance based on information entropy in complex networks

2013 ◽  
Vol 88 (6) ◽  
pp. 065201 ◽  
Author(s):  
Fan Wenli ◽  
Liu Zhigang ◽  
Hu Ping
Keyword(s):  
Complex Networks ◽  
Information Entropy ◽  
Node Importance

Node Importance Evaluation of Anti-Missile Networks

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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