A Novel Method Based on Node’s Correlation to Evaluate Important Nodes in Complex Networks

2021 ◽  
Author(s):  
Pengli Lu ◽  
Chen Dong ◽  
Yuhong Guo
Keyword(s):  
Complex Networks ◽  
Novel Method ◽  
Important Nodes

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.

Differential Evolution Dynamic Analysis in the Form of Complex Networks

2016 ◽  
pp. 285-318 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Complex Networks ◽  
Dynamic Analysis ◽  
Evolutionary Algorithm ◽  
Complex Network ◽  
Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

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 A novel method to identify multiple influential nodes in complex networks

2019 ◽  
Vol 49 (10) ◽  
pp. 1333-1342
Author(s):  
Xiaoyu LI ◽  
Mingyang ZHOU ◽  
Xiangyang WU ◽  
Binghong WANG ◽  
Liao LUO ◽  
...  
Keyword(s):  
Complex Networks ◽  
Novel Method ◽  
Influential Nodes

scholarly journals Identifying critical higher-order interactions in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mehmet Emin Aktas ◽  
Thu Nguyen ◽  
Sidra Jawaid ◽  
Rakin Riza ◽  
Esra Akbas
Keyword(s):  
Complex Networks ◽  
Higher Order ◽  
Power Grids ◽  
Giant Component ◽  
Centrality Measures ◽  
First Order ◽  
Pairwise Interactions ◽  
Size Of Giant Component ◽  
Novel Method ◽  
Structure And Functions

AbstractDiffusion on networks is an important concept in network science observed in many situations such as information spreading and rumor controlling in social networks, disease contagion between individuals, and cascading failures in power grids. The critical interactions in networks play critical roles in diffusion and primarily affect network structure and functions. While interactions can occur between two nodes as pairwise interactions, i.e., edges, they can also occur between three or more nodes, which are described as higher-order interactions. This report presents a novel method to identify critical higher-order interactions in complex networks. We propose two new Laplacians to generalize standard graph centrality measures for higher-order interactions. We then compare the performances of the generalized centrality measures using the size of giant component and the Susceptible-Infected-Recovered (SIR) simulation model to show the effectiveness of using higher-order interactions. We further compare them with the first-order interactions (i.e., edges). Experimental results suggest that higher-order interactions play more critical roles than edges based on both the size of giant component and SIR, and the proposed methods are promising in identifying critical higher-order interactions.

scholarly journals Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Longjie Li ◽  
Shenshen Bai ◽  
Mingwei Leng ◽  
Lu Wang ◽  
Xiaoyun Chen
Keyword(s):  
Decision Making ◽  
Complex Networks ◽  
Similarity Measure ◽  
Real World ◽  
Link Prediction ◽  
State Of The Art ◽  
Multiple Attribute Decision Making ◽  
Ideal Solution ◽  
Multiple Attribute ◽  
Novel Method

Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.

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.

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