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

2015 ◽  
Vol 29 (28) ◽  
pp. 1550168 ◽  
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.

scholarly journals A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

2021 ◽  
Vol 10 (2) ◽  
pp. 816-823
Keyword(s):  
Complex Networks ◽  
Time Complexity ◽  
Closeness Centrality ◽  
Clustering Coefficient ◽  
Centrality Measures ◽  
Degree Centrality ◽  
Computation Complexity ◽  
Influential Nodes ◽  
New Challenges ◽  
Virus Spreading

Influential nodes refer to the ability of a node to spread information in complex networks. Identifying influential nodes is an important problem in complex networks which plays a key role in many applications such as rumor controlling, virus spreading, viral market advertising, research paper views, and citations. Basic measures like degree centrality, betweenness centrality, closeness centrality are identifying influential nodes but they are incapable of largescale networks due to time complexity issues. Chen et al. [1] proposed semi-local centrality, which is reducing computation complexity and finding influential nodes in the network. Recently Yang et al. 2020 [2] proposed a novel centrality measure based on degree and clustering coefficient for identifying the influential nodes. Sanjay et al. 2020 [3] gave voterank and neighborhood coreness-based algorithms for finding the influenced nodes in the network. Zhiwei et al. 2019 [4] considered the average shortest path to discover the influenced node in the network. These are the few recent local,global and mixed centralities. In this paper, we show a broad view of recent methods for finding influential nodes in complex networks. It also analyzes the new challenges and limitations for a better understanding of each method in detail. The experimental results based on these methods show better performance compared with existing basic centrality measures.

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

2018 ◽  
Vol 32 (19) ◽  
pp. 1850216 ◽  
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.

scholarly journals Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

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.

A community-based approach to identify the most influential nodes in social networks

2016 ◽  
Vol 43 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Maryam Hosseini-Pozveh ◽  
Kamran Zamanifar ◽  
Ahmad Reza Naghsh-Nilchi
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Closeness Centrality ◽  
Centrality Measures ◽  
Community Based ◽  
Spreading Process ◽  
Centrality Measure ◽  
Influence Spread ◽  
The Social ◽  
Influential Nodes

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.

scholarly journals Controlling COVID-19 Propagation with Quarantine of Influential Nodes

2021 ◽  
Author(s):  
Sarkhosh S. Chaharborj ◽  
Shahriar S. Chaharborj ◽  
Phang Pei See
Keyword(s):  
Complex Network ◽  
Structural Organization ◽  
Centrality Measures ◽  
Topsis Method ◽  
Epidemic Disease ◽  
Dependability Analysis ◽  
Order Preference ◽  
Si Model ◽  
Influential Nodes ◽  
Important 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.

scholarly journals Efficiency centrality in time-varying graphs

2020 ◽  
Vol 12 (1) ◽  
pp. 5-21
Author(s):  
Péter Marjai ◽  
Attila Kiss
Keyword(s):  
Complex Networks ◽  
Betweenness Centrality ◽  
Cascade Model ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Time Varying ◽  
Independent Cascade Model ◽  
Independent Cascade ◽  
Influential Nodes ◽  
Time Varying Graphs

AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

A new method to identify influential nodes based on combining of existing centrality measures

2017 ◽  
Vol 31 (26) ◽  
pp. 1750243 ◽  
Author(s):  
Liguo Fei ◽  
Hongming Mo ◽  
Yong Deng
Keyword(s):  
Complex Networks ◽  
Lower Limit ◽  
Information Flow ◽  
New Method ◽  
Centrality Measures ◽  
Centrality Measure ◽  
Upper Limit ◽  
Open Issue ◽  
Novel Method ◽  
Influential Nodes

How to identify influential nodes in complex networks continues to be an open issue. A number of centrality measures have been presented to address this problem. However, these studies focus only on a centrality measure and each centrality measure has its own shortcomings and limitations. To solve the above problems, in this paper, a novel method is proposed to identify influential nodes based on combining of the existing centrality measures. Because information flow spreads in different ways in different networks, in the specific network, the appropriate centrality measures should be selected to calculate the ranking of nodes. Then, an interval can be generated for the ranking of each node, which includes the upper limit and lower limit obtained from different centrality measures. Next, the final ranking of each node can be determined based on the median of the interval. In order to illustrate the effectiveness of the proposed method, four experiments are conducted to identify vital nodes simulations on four real networks, and the superiority of the method can be demonstrated by the results of comparison experiments.

scholarly journals Identification of nodes influence based on global structure model in complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aman Ullah ◽  
Bin Wang ◽  
JinFang Sheng ◽  
Jun Long ◽  
Nasrullah Khan ◽  
...  
Keyword(s):  
Complex Networks ◽  
Disease Transmission ◽  
Closeness Centrality ◽  
Global Structure ◽  
Structure Model ◽  
Relevant Research ◽  
H Index ◽  
Influential Nodes ◽  
Influential Node ◽  
Global Influence

AbstractIdentification of Influential nodes in complex networks is challenging due to the largely scaled data and network sizes, and frequently changing behaviors of the current topologies. Various application scenarios like disease transmission and immunization, software virus infection and disinfection, increased product exposure and rumor suppression, etc., are applicable domains in the corresponding networks where identification of influential nodes is crucial. Though a lot of approaches are proposed to address the challenges, most of the relevant research concentrates only on single and limited aspects of the problem. Therefore, we propose Global Structure Model (GSM) for influential nodes identification that considers self-influence as well as emphasizes on global influence of the node in the network. We applied GSM and utilized Susceptible Infected Recovered model to evaluate its efficiency. Moreover, various standard algorithms such as Betweenness Centrality, Profit Leader, H-Index, Closeness Centrality, Hyperlink Induced Topic Search, Improved K-shell Hybrid, Density Centrality, Extended Cluster Coefficient Ranking Measure, and Gravity Index Centrality are employed as baseline benchmarks to evaluate the performance of GSM. Similarly, we used seven real-world and two synthetic multi-typed complex networks along-with different well-known datasets for experiments. Results analysis indicates that GSM outperformed the baseline algorithms in identification of influential node(s).

scholarly journals Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Aman Ullah ◽  
Bin wang ◽  
Jinfang Sheng ◽  
Jun Long ◽  
Nasrullah Khan
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Practical Significance ◽  
Centrality Measures ◽  
Sir Epidemic Model ◽  
Effective Distance ◽  
H Index ◽  
Sir Epidemic ◽  
Spreading Dynamics ◽  
Influential Nodes

Efficient identification of influential nodes is one of the essential aspects in the field of complex networks, which has excellent theoretical and practical significance in the real world. A valuable number of approaches have been developed and deployed in these areas where just a few have used centrality measures along with their concerning deficiencies and limitations in their studies. Therefore, to resolve these challenging issues, we propose a novel effective distance-based centrality (EDBC) algorithm for the identification of influential nodes in concerning networks. EDBC algorithm comprises factors such as the power of K-shell, degree nodes, effective distance, and numerous levels of neighbor’s influence or neighborhood potential. The performance of the proposed algorithm is evaluated on nine real-world networks, where a susceptible infected recovered (SIR) epidemic model is employed to examine the spreading dynamics of each node. Simulation results demonstrate that the proposed algorithm outperforms the existing techniques such as eigenvector, betweenness, closeness centralities, hyperlink-induced topic search, H-index, K-shell, page rank, profit leader, and gravity over a valuable margin.

A novel method to identify influential nodes in complex networks

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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