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

2016 ◽  
Author(s):  
Qin Qiong ◽  
Wang Dongxia
Keyword(s):  
Complex Networks ◽  
Evaluation Method ◽  
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.

Evaluation method for node importance based on attraction between nodes

2018 ◽  
Vol 29 (12) ◽  
pp. 1850125
Author(s):  
Jin Zeng ◽  
Chenxi Shao ◽  
Xingfu Wang ◽  
Fuyou Miao
Keyword(s):  
Complex Networks ◽  
Evaluation Method ◽  
Special Functions ◽  
Experimental Results ◽  
Node Importance ◽  
Theoretical Significance

Vital node, which has some special functions, plays an important role compared to other nodes in complex networks. Recently, the discovery of vital nodes in complex networks has captured increasing attention due to their important theoretical significance and great practicability. By defining the confidence of the node and the inter-node attraction, the significance of the node is measured by the product of the confidence of the node and the aggregation of attractions of the node on other nodes in the network. The experimental results illustrate that the proposed method has higher precision and performs well on various networks with different structures.

scholarly journals An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qibo Sun ◽  
Guoyu Yang ◽  
Ao Zhou
Keyword(s):  
Complex Networks ◽  
Disease Transmission ◽  
Evaluation Method ◽  
Weighted Average ◽  
Network Attack ◽  
Complex Network Theory ◽  
Node Importance ◽  
Different Characteristics ◽  
Important Nodes ◽  
Self Adaptive

Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

An Evaluation Method of Node Importance in Complex Network

2014 ◽  
Vol 602-605 ◽  
pp. 3597-3600
Author(s):  
Rui Sun ◽  
Wan Bo Luo
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Experimental Analysis ◽  
Evaluation Method ◽  
Theory And Practice ◽  
Theoretical Physics ◽  
Node Importance ◽  
Data Field ◽  
Meaningful Research

The evaluation of node importance is a very meaningful research in complex networks. This paper analyze the characteristics of complex network and consider the effects of nodes for the evaluation of node importance, introduces the idea of data field in theoretical physics and establishes the evaluation method of node importance based on topological potential in complex network. Through the theoretical and experimental analysis, it is proved that this method can evaluate the importance of node in complex network in a fast and accurate way, which is significant both to theory and practice.

A New Evaluation Method of Node Importance in Directed Weighted Complex Networks

2017 ◽  
Vol 5 (4) ◽  
pp. 367-375 ◽  
Author(s):  
Yu Wang ◽  
Jinli Guo ◽  
Han Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Evaluation Method ◽  
Network Models ◽  
Node Importance ◽  
Weighted Complex Network ◽  
Complex Network Models ◽  
Evaluation Matrix ◽  
Key Nodes

AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

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

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 A Resilience Approach to Symbiosis Networks of Ecoindustrial Parks Based on Cascading Failure Model

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Zeng ◽  
Renbin Xiao ◽  
Xiangmei Li
Keyword(s):  
Theoretical Analysis ◽  
Evaluation Method ◽  
Shanxi Province ◽  
Critical Threshold ◽  
Cascading Failure ◽  
Load Parameter ◽  
Failure Model ◽  
Node Importance ◽  
Industrial Parks ◽  
Ecoindustrial Parks

Evaluation and improvement of resilience in ecoindustrial parks have been the pressing issues to be addressed in the study of safety. In this paper, eco-industrial systems are extracted as symbiosis networks by using social network analysis first. We then construct a novel cascading failure model and propose an evaluation method of node importance according to the features of symbiosis networks of eco-industrial parks. Based on the cascading model, an effective new method, that is, the critical threshold, is put forward to quantitatively assess the resilience of symbiosis networks of eco-industrial parks. Some theoretical analysis is furthermore provided to the critical threshold. Finally, we take Jinjie eco-industrial system in Shanxi Province of China as a case to investigate its resilience. The key potential nodes are identified by using our model. We also find the respective relation among the resilience of symbiosis networks and the parameters in our cascading model. Theoretical analysis results and numerical simulations both show the optimal value of the tunable load parameter with which the strongest resilience level against cascading failures can be attained in symbiosis networks of eco-industrial parks.

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