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).

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

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 Identifying Influential Nodes of Complex Networks Based on Trust-Value

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 280
Author(s):  
Jinfang Sheng ◽  
Jiafu Zhu ◽  
Yayun Wang ◽  
Bin Wang ◽  
Zheng’ang Hou
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Trust Value ◽  
The Real ◽  
The World ◽  
Attribute Information ◽  
Similarity Ratio ◽  
Influential Nodes ◽  
Influential Node

The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.

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 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 Hunting for Influential Nodes in Complex Networks Using Local Information

2021 ◽  
Author(s):  
Zhihao Dong ◽  
Yuanzhu Chen ◽  
Terrence S. Tricco ◽  
Cheng Li ◽  
Ting Hu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Structure And Function ◽  
Epidemic Threshold ◽  
Global Knowledge ◽  
The Real ◽  
Influential Nodes ◽  
Influential Node ◽  
And Function ◽  
High Degree

Abstract Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.

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.

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.

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