scholarly journals Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1570 ◽  
Author(s):  
Jingcheng Zhu ◽  
Lunwen Wang
Keyword(s):  
Complex Networks ◽  
Sir Model ◽  
Identification Accuracy ◽  
Degree Centrality ◽  
Network Stability ◽  
Propagation Process ◽  
Calculation Results ◽  
Influential Nodes ◽  
Nearest Neighborhood ◽  
Accuracy Degree

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

2018 ◽  
Vol 32 (06) ◽  
pp. 1850118 ◽  
Author(s):  
Mengtian Li ◽  
Ruisheng Zhang ◽  
Rongjing Hu ◽  
Fan Yang ◽  
Yabing Yao ◽  
...  
Keyword(s):  
Complex Networks ◽  
Nearest Neighbors ◽  
Sir Model ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Competitive Performance ◽  
Spreading Process ◽  
Degree Sum ◽  
Local Degree ◽  
Crucial Problem

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

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.

scholarly journals Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

2022 ◽  
Vol 9 ◽  
Author(s):  
Li Tao ◽  
Mutong Liu ◽  
Zili Zhang ◽  
Liang Luo
Keyword(s):  
Complex Networks ◽  
Independent Set ◽  
Sir Model ◽  
Second Order ◽  
Propagation Process ◽  
Ranking Problem ◽  
Local Index ◽  
Practical Applications ◽  
Node Ranking ◽  
New Algorithms

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

scholarly journals Locating Multiple Sources of Contagion in Complex Networks under the SIR Model

2019 ◽  
Vol 9 (20) ◽  
pp. 4472 ◽  
Author(s):  
Xiang Li ◽  
Yangyang Liu ◽  
Chengli Zhao ◽  
Xue Zhang ◽  
Dongyun Yi
Keyword(s):  
Complex Networks ◽  
Incomplete Information ◽  
Human Life ◽  
Sir Model ◽  
Multiple Sources ◽  
Propagation Process ◽  
Localization Accuracy ◽  
Localization Method ◽  
Label Method ◽  
New Perspective

Simultaneous outbreaks of contagion are a great threat against human life, resulting in great panic in society. It is urgent for us to find an efficient multiple sources localization method with the aim of studying its pathogenic mechanism and minimizing its harm. However, our ability to locate multiple sources is strictly limited by incomplete information about nodes and the inescapable randomness of the propagation process. In this paper, we present a valid approach, namely the Potential Concentration Label method, which helps locate multiple sources of contagion faster and more accurately in complex networks under the SIR(Susceptible-Infected-Recovered) model. Through label assignment in each node, our aim is to find the nodes with maximal value after several iterations. The experiments demonstrate that the accuracy of our multiple sources localization method is high enough. With the number of sources increasing, the accuracy of our method declines gradually. However, the accuracy remains at a slight fluctuation when average degree and network scale make a change. Moreover, our method still keeps a high multiple sources localization accuracy with noise of various intensities, which shows its strong anti-noise ability. I believe that our method provides a new perspective for accurate and fast multi-sources localization in complex networks.

Identifying influential nodes in complex networks based on expansion factor

2016 ◽  
Vol 27 (09) ◽  
pp. 1650105 ◽  
Author(s):  
Dong Liu ◽  
Yun Jing ◽  
Baofang Chang
Keyword(s):  
Complex Networks ◽  
Time Complexity ◽  
Sir Model ◽  
The Other ◽  
Practical Significance ◽  
Expansion Factor ◽  
Influential Nodes ◽  
Simulation Results

Identifying the top influential spreaders in a network has practical significance. In this paper, we propose a novel centrality to identify influential spreaders based on expansion factor. Nodes with high expansion factor centrality (EFC) have strong spreading capability. During the course of the work, an improved strategy is proposed to reduce the time complexity of EFC. We discuss the correlations between EFC and the other five classical indicators. Simulation results on the Susceptible-Infected-Removed (SIR) model manifest that EFC can identify influential nodes and find some critical influential nodes neglected by other indicators.

Effect of rich-club on diffusion in complex networks

2018 ◽  
Vol 32 (12) ◽  
pp. 1850142 ◽  
Author(s):  
Kamal Berahmand ◽  
Negin Samadi ◽  
Seyed Mahmood Sheikholeslami
Keyword(s):  
Complex Networks ◽  
Time Complexity ◽  
Linear Time ◽  
Local Information ◽  
Degree Centrality ◽  
Rich Club ◽  
Influential Nodes ◽  
The Rich ◽  
Artificial Datasets

One of the main issues in complex networks is the phenomenon of diffusion in which the goal is to find the nodes with the highest diffusing power. In diffusion, there is always a conflict between accuracy and efficiency time complexity; therefore, most of the recent studies have focused on finding new centralities to solve this problem and have offered new ones, but our approach is different. Using one of the complex networks’ features, namely the “rich-club”, its effect on diffusion in complex networks has been analyzed and it is demonstrated that in datasets which have a high rich-club, it is better to use the degree centrality for finding influential nodes because it has a linear time complexity and uses the local information; however, this rule does not apply to datasets which have a low rich-club. Next, real and artificial datasets with the high rich-club have been used in which degree centrality has been compared to famous centrality using the SIR standard.

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 Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Zelong Yi ◽  
Xiaokun Wu ◽  
Fan Li
Keyword(s):  
Complex Networks ◽  
Information Diffusion ◽  
Decomposition Method ◽  
Social Ties ◽  
New Products ◽  
Sir Model ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Weighted Sum ◽  
Virus Spread

Identifying influential spreaders in complex networks is crucial for containing virus spread, accelerating information diffusion, and promoting new products. In this paper, inspired by the effect of leaders on social ties, we propose the most influential neighbors’ k-shell index that is the weighted sum of the products between k-core values of itself and the node with the maximum k-shell values. We apply the classical Susceptible-Infected-Recovered (SIR) model to verify the performance of our method. The experimental results on both real and artificial networks show that the proposed method can quantify the node influence more accurately than degree centrality, betweenness centrality, closeness centrality, and k-shell decomposition method.

scholarly journals Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

2019 ◽  
Vol 33 (32) ◽  
pp. 1950395 ◽  
Author(s):  
Pengli Lu ◽  
Chen Dong
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Local Structure ◽  
Sir Model ◽  
Degree Centrality ◽  
Excellent Performance ◽  
H Index ◽  
Evaluation Standard ◽  
Node Importance ◽  
Key Nodes

The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequences. In this paper, we proposed the clustering H-index mixing (CHM) centrality based on the H-index of the node itself and the relative distance of its neighbors. Starting from the node itself and combining with the topology around the node, the importance of the node and its spreading capability were determined. In order to evaluate the performance of the proposed method, we use Susceptible–Infected–Recovered (SIR) model, monotonicity and resolution as the evaluation standard of experiment. Experimental results in artificial networks and real-world networks show that CHM centrality has excellent performance in identifying node importance and its spreading capability.

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