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.

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.

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.

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

2015 ◽  
Vol 29 (03) ◽  
pp. 1450268 ◽  
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.

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 Seeking the Important Nodes of Complex Networks in Product R&D Team Based on Fuzzy AHP and TOPSIS

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhang ◽  
Qingpu Zhang ◽  
Hamidreza Karimi
Keyword(s):  
Complex Networks ◽  
Fuzzy Ahp ◽  
Evaluation Criteria ◽  
Multiple Attribute Decision Making ◽  
Eigenvector Centrality ◽  
Analytic Hierarchy ◽  
Node Importance ◽  
Multiple Attribute ◽  
Order Preference ◽  
Important Nodes

How to seek the important nodes of complex networks in product research and development (R&D) team is particularly important for companies engaged in creativity and innovation. The previous literature mainly uses several single indicators to assess the node importance; this paper proposes a multiple attribute decision making model to tentatively solve these problems. Firstly, choose eight indicators as the evaluation criteria, four from centralization of complex networks: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality and four from structural holes of complex networks: effective size, efficiency, constraint, and hierarchy. Then, use fuzzy analytic hierarchy process (AHP) to obtain the weights of these indicators and use technique for order preference by similarity to an ideal solution (TOPSIS) to assess the importance degree of each node of complex networks. Finally, taking a product R&D team of a game software company as a research example, test the effectiveness, operability, and efficiency of the method we established.

Efficient Identification of Node Importance Based on Agglomeration in Cycle-Related Networks

2020 ◽  
Vol 31 (07) ◽  
pp. 969-978
Author(s):  
Aysun Asena Kunt ◽  
Zeynep Nihan Berberler
Keyword(s):  
Complex Networks ◽  
Network Robustness ◽  
Practical Significance ◽  
Contraction Method ◽  
Node Importance ◽  
Different Types ◽  
Novel Method ◽  
Important Nodes

The identification of node importance in complex networks is of theoretical and practical significance for improving network robustness and invulnerability. In this paper, the importance of each node is evaluated and important nodes are identified in cycles and related networks by node contraction method based on network agglomeration. This novel method considers both the degree and the position of the node for the identifying the importance of the node. The effectiveness and the feasibility of this method was also validated through experiments on different types of complex networks.

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.

Identifying vital nodes in complex networks based on information entropy, minimum dominating set and distance

2021 ◽  
pp. 2150071
Author(s):  
Pengli Lu ◽  
Wei chen
Keyword(s):  
Complex Networks ◽  
Information Entropy ◽  
Disease Transmission ◽  
Dominating Set ◽  
Small World ◽  
Simulation Experiments ◽  
Network Attack ◽  
Scale Free ◽  
Minimum Dominating Set ◽  
Key Nodes

Identifying the vital nodes in complex networks is essential in disease transmission control and network attack protection. In this paper, in order to identify the vital nodes, we define a centrality method named EMDC, which is based on information entropy, minimum dominating set (MDS) and the distance between node pairs. This method calculates the local spreading capability (LSC) of node by information entropy and selects that nodes have the largest value of LSC as core nodes by MDS. Then it defines the node’s spreading capability (SC) to use the sum of weighted distances from a node to the core nodes. Finally, the nodes are ranked by considering SC of their neighbors. The key nodes can be further identified in complex networks. In order to verify the effectiveness of this method, key nodes identification simulation experiments are carried out on 11 real networks, Scale-Free (BA) networks and Small-World (WS) networks, respectively. Experimental results show that this method can more effectively identify the influence of nodes in the networks.

Sign in / Sign up

Export Citation Format

Share Document