Brittleness analysis and important nodes discovery in large time-evolving complex networks

2017 ◽  
Vol 22 (1) ◽  
pp. 50-54 ◽  
Author(s):  
Hong Zhang ◽  
Changzhen Hu ◽  
Xiaojun Wang
Keyword(s):  
Complex Networks ◽  
Large Time ◽  
Important Nodes

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.

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 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.

scholarly journals Identifying important nodes affecting network security in complex networks

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199928
Author(s):  
Yongshan Liu ◽  
Jianjun Wang ◽  
Haitao He ◽  
Guoyan Huang ◽  
Weibo Shi
Keyword(s):  
Network Security ◽  
Complex Networks ◽  
Monitoring Network ◽  
Security Evaluation ◽  
Identification Algorithm ◽  
Information Control ◽  
Network Efficiency ◽  
Structural Hole ◽  
Local Propagation ◽  
Important Nodes

An important node identification algorithm based on an improved structural hole and K-shell decomposition algorithm is proposed to identify important nodes that affect security in complex networks. We consider the global structure of a network and propose a network security evaluation index of important nodes that is free of prior knowledge of network organization based on the degree of nodes and nearest neighborhood information. A node information control ability index is proposed according to the structural hole characteristics of nodes. An algorithm ranks the importance of nodes based on the above two indices and the nodes’ local propagation ability. The influence of nodes on network security and their own propagation ability are analyzed by experiments through the evaluation indices of network efficiency, network maximum connectivity coefficient, and Kendall coefficient. Experimental results show that the proposed algorithm can improve the accuracy of important node identification; this analysis has applications in monitoring network security.

scholarly journals Influential Nodes Identification in Complex Networks via Information Entropy

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 242 ◽  
Author(s):  
Chungu Guo ◽  
Liangwei Yang ◽  
Xiao Chen ◽  
Duanbing Chen ◽  
Hui Gao ◽  
...  
Keyword(s):  
Complex Networks ◽  
Simulation Model ◽  
Information Entropy ◽  
Attenuation Factor ◽  
Scientific Publications ◽  
Dynamic Update ◽  
Influential Nodes ◽  
Update Strategy ◽  
Specific Number ◽  
Important Nodes

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes’ spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.

scholarly journals Mining Important Nodes in Directed Weighted Complex Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yunyun Yang ◽  
Gang Xie ◽  
Jun Xie
Keyword(s):  
Complex Networks ◽  
Performance Indicators ◽  
Network Data ◽  
Numerical Examples ◽  
Frequency Of Contact ◽  
The Impact ◽  
Important Nodes ◽  
Weighted Complex Networks

In complex networks, mining important nodes has been a matter of concern by scholars. In recent years, scholars have focused on mining important nodes in undirected unweighted complex networks. But most of the methods are not applicable to directed weighted complex networks. Therefore, this paper proposes a Two-Way-PageRank method based on PageRank for further discussion of mining important nodes in directed weighted complex networks. We have mainly considered the frequency of contact between nodes and the length of time of contact between nodes. We have considered the source of the nodes (in-degree) and the whereabouts of the nodes (out-degree) simultaneously. We have given node important performance indicators. Through numerical examples, we analyze the impact of variation of some parameters on node important performance indicators. Finally, the paper has verified the accuracy and validity of the method through empirical network data.

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