Influential nodes in planar trusses and meshes using centrality measures

2021 ◽  
Author(s):  
P. Sangeetha ◽  
R. Sundareswaran ◽  
M. Shanmugapriya ◽  
S. Srinidhi ◽  
K. Sowmya
Keyword(s):  
Centrality Measures ◽  
Influential Nodes

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.

scholarly journals Spheres of Legislation: Polarization and Most Influential Nodes in Behavioral Context

2020 ◽  
Author(s):  
Andrew C. Phillips ◽  
Mohammad T. Irfan ◽  
Luca Ostertag-Hill
Keyword(s):  
Network Structure ◽  
Clustering Algorithms ◽  
Generative Models ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Behavioral Context ◽  
Real World Data ◽  
Game Theoretic ◽  
Influential Nodes ◽  
Structural Centrality

Abstract Game-theoretic models of influence in networks often assume the network structure to be static. In this paper, we allow the network structure to vary according to the underlying behavioral context. This leads to several interesting questions on two fronts. First, how do we identify different contexts and learn the corresponding network structures using real-world data? We focus on the U.S. Senate and apply unsupervised machine learning techniques, such as fuzzy clustering algorithms and generative models, to identify spheres of legislation as context and learn an influence network for each sphere. Second, how do we analyze these networks in order to gain an insight into the role played by the spheres of legislation in various interesting constructs like polarization and most influential nodes? To this end, we apply both game-theoretic and social network analysis techniques. In particular, we show that game-theoretic notion of most influential nodes brings out the strategic aspects of interactions like bipartisan grouping, which structural centrality measures fail to capture.

Multimodal ensemble approach to identify and rank top-k influential nodes of scholarly literature using Twitter network

2019 ◽  
Vol 46 (4) ◽  
pp. 437-458 ◽  
Author(s):  
Bharat Tidke ◽  
Rupa Mehta ◽  
Jenish Dhanani
Keyword(s):  
Computational Complexity ◽  
Social Networking Sites ◽  
Majority Voting ◽  
Experimental Result ◽  
Centrality Measures ◽  
Data Set ◽  
Scholarly Literature ◽  
Ensemble Approach ◽  
Nature Of Information ◽  
Influential Nodes

Scholarly literature is an immense network of activities, linked via collaborations or information propagation. Analysing such network can be leveraged by harnessing rich semantic meaning of scholarly graph. Identifying and ranking top- k influential nodes from various domains of scholarly literature using social media data are still infancy. Social networking sites like Twitter provide an opportunity to create inventive graph-based measures to identify and rank influential nodes such as scholars, articles, journal, information spreading media and academic institutions of scholarly literature. Many network-based models such as centrality measures have been proposed to identify influential nodes. The empirical annotation shows that centrality measures for finding influential nodes are high in computational complexity. In addition, notion of these measures have high variance, which signifies an influential node deviation with change in application and nature of information flows in the network. The research aims to propose an ensemble learning approach based on multimodal majority voting influence (MMMVI) to identify and weighted multimodal ensemble average influence (WMMEAI) to rank top- k influential nodes in Twitter network data set of well-known three influential nodes, that is, academic institution, scholar and journal. The empirical analysis has been accomplished to learn practicability and efficiency of the proposed approaches when compared with state-of-the-art approaches. The experimental result shows that the ensemble approach using surface learning models (SLMs) can lead to better identification and ranking of influential nodes with low computational complexity.

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 Influential Nodes in the OBOR Fossil Energy Trade Network Based on D-S Theory: Detection and Evolution Analysis

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Cuixia Gao ◽  
Simin Tao ◽  
Kehu Li ◽  
Yuyang He
Keyword(s):  
Evidence Theory ◽  
Initial Result ◽  
Growth Potential ◽  
Data Uncertainty ◽  
Centrality Measures ◽  
Network Efficiency ◽  
Fossil Energy ◽  
Resource Endowment ◽  
Energy Trade ◽  
Influential Nodes

The structure formed by fossil energy trade among countries can be divided into multiple subcommodity networks. However, the difference of coupling mode and transmission mechanism between layers of the multirelationship network will affect the measurement of node importance. In this paper, a framework of multisource information fusion by considering data uncertainty and the classical network centrality measures is build. Then, the evidential centrality (EVC) indicator is proposed, by integrating Dempster–Shafer evidence theory and network theory, to empirically identify influential nodes of fossil energy trade along the Belt and Road Initiative. The initial result of the heterogeneity characteristics of the constructed network drives us to explore the core node issue further. The main detected evidential nodes include Russia, Kazakhstan, Czechia, Slovakia, Egypt, Romania, China, Saudi Arabia, and Singapore, which also have higher impact on network efficiency. In addition, cluster analysis discovered that resource endowment is an essential factor influencing country’s position, followed by geographical distance, economic level, and economic growth potential. Therefore, the above aspects should be considered when ensuring national trade security. At last, the rationality and comprehensiveness of EVC are verified by comparing with some benchmark centralities.

Centrality Measures in Finding Influential Nodes for the Big-Data Network

2021 ◽  
pp. 1-17
Author(s):  
Sathyanarayanan Gopalakrishnan ◽  
Supriya Sridharan ◽  
Swaminathan Venkatraman
Keyword(s):  
Big Data ◽  
Centrality Measures ◽  
Data Network ◽  
Influential Nodes

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.

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.

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