Controlling COVID-19 Propagation with Quarantine of Influential Nodes

Abstract We study importance of influential nodes in spreading of epidemic COVID-19 in a complex network. We will show that quarantine of important and influential nodes or consider of health protocols by efficient nodes is very helpful and effective in the controlling of spreading epidemic COVID-19 in a complex network. Therefore, identifying influential nodes in complex networks is the very significant part of dependability analysis, which has been a clue matter in analyzing the structural organization of a network. The important nodes can be considered as a person or as an organization. To find the influential nodes we use the technique for order preference by similarity to ideal solution (TOPSIS) method with new proposed formula to obtain the efficient weights. We use various centrality measures as the multi-attribute of complex network in the TOPSIS method. We define a formula for spreading probability of epidemic disease in a complex network to study the power of infection spreading with quarantine of important nodes. In the following, we use the Susceptible–Infected (SI) model to figure out the performance and efficiency of the proposed methods. The proposed method has been examined for efficiency and practicality using numerical examples.

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A dynamic weighted TOPSIS method for identifying influential nodes in complex networks

Modern Physics Letters B ◽

10.1142/s0217984918502160 ◽

2018 ◽

Vol 32 (19) ◽

pp. 1850216 ◽

Cited By ~ 4

Author(s):

Pingle Yang ◽

Xin Liu ◽

Guiqiong Xu

Keyword(s):

Complex Networks ◽

Centrality Measures ◽

Topsis Method ◽

Decision Making Problem ◽

Significant Research ◽

Order Preference ◽

Multi Attribute Decision Making ◽

Influential Nodes ◽

Influential Node ◽

Grey Relational

Identifying the influential nodes in complex networks is a challenging and significant research topic. Though various centrality measures of complex networks have been developed for addressing the problem, they all have some disadvantages and limitations. To make use of the advantages of different centrality measures, one can regard influential node identification as a multi-attribute decision-making problem. In this paper, a dynamic weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is developed. The key idea is to assign the appropriate weight to each attribute dynamically, based on the grey relational analysis method and the Susceptible–Infected–Recovered (SIR) model. The effectiveness of the proposed method is demonstrated by applications to three actual networks, which indicates that our method has better performance than single indicator methods and the original weighted TOPSIS method.

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Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

Vietnam Journal of Computer Science ◽

10.1142/s2196888821500032 ◽

2020 ◽

Vol 08 (01) ◽

pp. 93-112

Author(s):

Péter Marjai ◽

Attila Kiss

Keyword(s):

Diffusion Process ◽

Complex Networks ◽

Relative Entropy ◽

Information Diffusion ◽

Dynamic Networks ◽

Centrality Measures ◽

Topsis Method ◽

Cover Time ◽

Influential Nodes ◽

Different Characteristics

For decades, centrality has been one of the most studied concepts in the case of complex networks. It addresses the problem of identification of the most influential nodes in the network. Despite the large number of the proposed methods for measuring centrality, each method takes different characteristics of the networks into account while identifying the “vital” nodes, and for the same reason, each has its advantages and drawbacks. To resolve this problem, the TOPSIS method combined with relative entropy can be used. Several of the already existing centrality measures have been developed to be effective in the case of static networks, however, there is an ever-increasing interest to determine crucial nodes in dynamic networks. In this paper, we are investigating the performance of a new method that identifies influential nodes based on relative entropy, in the case of dynamic networks. To classify the effectiveness, the Suspected-Infected model is used as an information diffusion process. We are investigating the average infection capacity of ranked nodes, the Time-Constrained Coverage as well as the Cover Time.

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Multi-attribute ranking method for identifying key nodes in complex networks based on GRA

International Journal of Modern Physics B ◽

10.1142/s0217979218503630 ◽

2018 ◽

Vol 32 (32) ◽

pp. 1850363 ◽

Cited By ~ 2

Author(s):

Pingle Yang ◽

Guiqiong Xu ◽

Huiping Chen

Keyword(s):

Complex Networks ◽

Centrality Measures ◽

Topsis Method ◽

Node Importance ◽

Significant Research ◽

Order Preference ◽

Limited Application ◽

Multi Attribute Decision Making ◽

Grey Relational ◽

Key Nodes

How to identify key nodes is a challenging and significant research issue in complex networks. Some existing evaluation indicators of node importance have the disadvantages of limited application scope and one-sided evaluation results. This paper takes advantage of multiple centrality measures comprehensively, by regarding the identification of key nodes as a multi-attribute decision making (MADM) problem. Firstly, a new local centrality (NLC) measure is put forward through considering multi-layer neighbor nodes and clustering coefficients. Secondly, combining the grey relational analysis (GRA) method and the susceptible-infectious-recovered (SIR) model, a modified dynamic weighted technique for order preference by similarity to ideal solution (TOPSIS) method is proposed. Finally, the effectiveness of the NLC is illustrated by applications to nine actual networks. Furthermore, the experimental results on four actual networks demonstrate that the proposed method can identify key nodes more accurately than the existing weighted TOPSIS method.

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Identifying Important Nodes in Complex Networks Based on Multiattribute Evaluation

Mathematical Problems in Engineering ◽

10.1155/2018/8268436 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11

Author(s):

Hui Xu ◽

Jianpei Zhang ◽

Jing Yang ◽

Lijun Lun

Keyword(s):

Complex Network ◽

Topological Structure ◽

Decomposition Method ◽

Large Scale ◽

System Structure ◽

Practical Significance ◽

Centrality Measures ◽

Actual System ◽

Efficient System ◽

Important Nodes

Assessing and measuring the importance of nodes in a complex network are of great theoretical and practical significance to improve the robustness of the actual system and to design an efficient system structure. The classical local centrality measures of important nodes only take the number of node neighbors into consideration but ignore the topological relations and interactions among neighbors. Due to the complexity of the algorithm itself, the global centrality measure cannot be applied to the analysis of large-scale complex network. The k-shell decomposition method considers the core node located in the center of the network as the most important node, but it only considers the residual degree and neglects the interaction and topological structure between the node and its neighbors. In order to identify the important nodes efficiently and accurately in the network, this paper proposes a local centrality measurement method based on the topological structure and interaction characteristics of the nodes and their neighbors. On the basis of the k-shell decomposition method, the method we proposed introduces two properties of structure hole and degree centrality, which synthetically considers the nodes and their neighbors’ network location information, topological structure, scale characteristics, and the interaction between different nuclear layers of them. In this paper, selective attacks on four real networks are, respectively, carried out. We make comparative analyses of the averagely descending ratio of network efficiency between our approach and other seven indices. The experimental results show that our approach is valid and feasible.

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Network Analysis Based on Important Node Selection and Community Detection

Mathematics ◽

10.3390/math9182294 ◽

2021 ◽

Vol 9 (18) ◽

pp. 2294

Author(s):

Attila Mester ◽

Andrei Pop ◽

Bogdan-Eduard-Mădălin Mursa ◽

Horea Greblă ◽

Laura Dioşan ◽

...

Keyword(s):

Complex Network ◽

Community Detection ◽

Centrality Measures ◽

Complementary Information ◽

Detection Algorithms ◽

Node Importance ◽

Dual Perspective ◽

The Stability ◽

Important Nodes ◽

Empirical Means

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

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A novel measure to identify influential nodes in complex networks based on network global efficiency

Modern Physics Letters B ◽

10.1142/s0217984915501687 ◽

2015 ◽

Vol 29 (28) ◽

pp. 1550168 ◽

Cited By ~ 3

Author(s):

Tingping Zhang ◽

Bin Fang ◽

Xinyu Liang

Keyword(s):

Complex Networks ◽

Closeness Centrality ◽

Centrality Measures ◽

Dynamical Process ◽

Static Structure ◽

Structure And Dynamics ◽

Global Efficiency ◽

Si Model ◽

Influential Nodes ◽

Structure Properties

Identifying influential nodes is a basic measure of characterizing the structure and dynamics in complex networks. In this paper, we use network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks. Differing from the traditional network global efficiency, the proposed measure is determined by removing edges from networks, not removing nodes. Instead of static structure properties which are exhibited by other traditional centrality measures, such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), we focus on the perspective of dynamical process and global structure in complex networks. Susceptible-infected (SI) model is utilized to evaluate the performance of the proposed method. Experimental results show that the proposed measure is more effective than the other three centrality measures.

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Determination and Identification of Important and Influential Nodes Involved in the Pathology of Escherichia Coli Using Improved TOPSIS Method

10.21203/rs.2.18279/v1 ◽

2019 ◽

Author(s):

zohreh minaei

Keyword(s):

Escherichia Coli ◽

Hybrid System ◽

Biological Networks ◽

Promising Method ◽

Topsis Method ◽

Influential Nodes ◽

Important Nodes ◽

Primary Index

Abstract Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. Thus, important nodes of the network can be identified by analyzing some of the centrality extracted from the network. In this paper, we aim to identify the important proteins in the Escherichia Coli (E.Coli) network based on extraction of centralities. During these operations, centralities such as degree of centrality, betweeness, laplacian and closeness, are considered as node's important indicators. Finally the important nodes will be determined based on the centrality and Technique for order performance by similarity (TOPSIS) method. After performing the weighted TOPSIS simulation and obtaining the output result, it was found that the proposed hybrid system is able to place 74 and 99 important nodes between the top 100 and 150 nodes, respectively. Finally, the results of this study are compared with other similar studies.

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A novel method to identify influential nodes in complex networks

International Journal of Modern Physics C ◽

10.1142/s0129183120500229 ◽

2019 ◽

Vol 31 (02) ◽

pp. 2050022

Author(s):

Yuanzhi Yang ◽

Lei Yu ◽

Xing Wang ◽

Siyi Chen ◽

You Chen ◽

...

Keyword(s):

Complex Networks ◽

Comprehensive Evaluation ◽

Ideal Solution ◽

Decision Matrix ◽

Single Measure ◽

Order Preference ◽

Si Model ◽

Novel Method ◽

Influential Nodes ◽

Improved Technology

Identifying influential nodes in complex networks continues to be an open and vital issue, which is of great significance to the robustness and vulnerability of networks. In order to accurately identify influential nodes in complex networks and avoid the deviation in the evaluation of node influence by single measure, a novel method based on improved Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to integrate multiple measures and identify influential nodes. Our method takes into account degree centrality (DC), closeness centrality (CC) and betweenness centrality (BC), and uses the information of the decision matrix to objectively assign weight to each measure, and takes the closeness degree from each node to be the ideal solution as the basis for comprehensive evaluation. At last, four experiments based on the Susceptible-Infected (SI) model are carried out, and the superiority of our method can be demonstrated.

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