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.

scholarly journals The Local Triangle Structure Centrality Method to Rank Nodes in Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaojian Ma ◽  
Yinghong Ma
Keyword(s):  
Real World ◽  
Local Structure ◽  
Sir Model ◽  
Evaluation Methods ◽  
Ranking Method ◽  
Information Propagation ◽  
Practical Application ◽  
H Index ◽  
Simulation Results ◽  
Important Nodes

Detecting influential spreaders had become a challenging and crucial topic so far due to its practical application in many areas, such as information propagation inhibition and disease dissemination control. Some traditional local based evaluation methods had given many discussions on ranking important nodes. In this paper, ranking nodes of networks continues to be discussed. A semilocal structures method for ranking nodes based on the degree and the neighbors’ connections of the node is presented. The semilocal structures are regarded as the number of neighbors of the nodes and the connections between the node and its neighbors. We combined the triangle structure and the degree information of the neighbors to define the inner-outer spreading ability of the nodes and then summed the node neighbors’ inner-outer spreading ability to be used as the local triangle structure centrality (LTSC). The LTSC avoids the defect “pseudo denser connections” in measuring the structure of neighbors. The performance of the proposed LTSC method is evaluated by comparing the spreading ability on both real-world and synthetic networks with the SIR model. The simulation results of the discriminability and the correctness compared with pairs of ranks (one is generated by SIR model and the others are generated by central nodes measures) show that LTSC outperforms some other local or semilocal methods in evaluating the node’s influence in most cases, such as degree, betweenness, H-index, local centrality, local structure centrality, K-shell, and S-shell. The experiments prove that the LTSC is an efficient and accurate ranking method which provides a more reasonable evaluating index to rank nodes than some previous approaches.

A New Evaluation Method of Node Importance in Directed Weighted Complex Networks

2017 ◽  
Vol 5 (4) ◽  
pp. 367-375 ◽  
Author(s):  
Yu Wang ◽  
Jinli Guo ◽  
Han Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Evaluation Method ◽  
Network Models ◽  
Node Importance ◽  
Weighted Complex Network ◽  
Complex Network Models ◽  
Evaluation Matrix ◽  
Key Nodes

AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

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.

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

2018 ◽  
Vol 32 (32) ◽  
pp. 1850363 ◽  
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.

Community cores expansion for overlapping community detection in complex networks

2018 ◽  
Vol 32 (33) ◽  
pp. 1850405 ◽  
Author(s):  
Yongjie Yan ◽  
Guang Yu ◽  
Xiangbin Yan ◽  
Hui Xie
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Local Structure ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Clique Percolation ◽  
Overlapping Community ◽  
Synthetic Datasets

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

Weighted synthetical influence of degree and H-index in link prediction of complex networks

2020 ◽  
Vol 34 (31) ◽  
pp. 2050307
Author(s):  
Shu Shan Zhu ◽  
Wenya Li ◽  
Ning Chen ◽  
Xuzhen Zhu ◽  
Yuxin Wang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Link Prediction ◽  
Prediction Models ◽  
Great Success ◽  
H Index ◽  
Topological Similarity ◽  
Influence Measurement

Link prediction based on traditional models have attracted many interests recently. Among all models, the ones based on topological similarity have achieved great success. However, researchers pay more attention to links, but less to endpoint influence. After profound investigation, we find that the synthesis of degree and H-index plays an important role in modeling endpoint influence. So, in this paper, we propose link prediction models based on weighted synthetical influence, exploring the role of H-index and degree in endpoint influence measurement. Experiments on 12 real-world networks show that the proposed models can provide higher accuracy.

scholarly journals Complex Network Filtering and Compression Algorithm Based on Triangle-Subgraph

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shuxia Ren ◽  
Tao Wu ◽  
Shubo Zhang
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Sir Model ◽  
Compression Algorithm ◽  
Sorting Algorithm ◽  
Ranking Algorithm ◽  
Original Network ◽  
Node Importance ◽  
Importance Ranking ◽  
Result Analysis

Compressing the data of a complex network is important for visualization. Based on the triangle-subgraph structure in complex networks, complex network filtering compression algorithm based on the triangle-subgraph is proposed. The algorithm starts from the edge, lists nodes of the edge and their common node sets to form a triangle-subgraph set, parses the triangle-subgraph set, and constructs new complex network to complete compression. Before calculating the set of triangle-subgraph, node importance ranking algorithm is proposed to extract high- and low-importance nodes and filter them to reduce computational scale of complex networks. Experimental results show that filtering compression algorithm can not only improve the compression rate but also retain information of the original network at the same time; sorting result analysis and SIR model analysis show that the sorting result of node importance sorting algorithm has accuracy and rationality.

Local volume dimension: A novel approach for important nodes identification in complex networks

2021 ◽  
pp. 2150069
Author(s):  
Hanwen Li ◽  
Yong Deng
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Open Problem ◽  
Sir Model ◽  
Local Volume ◽  
Novel Approach ◽  
Si Model ◽  
Important Nodes

How to identify important nodes in complex networks? It is still an open problem. Many methods have been proposed to tackle this problem. The main contribution of this paper is to propose a method to identify important nodes based on local volume dimension (LVD). If the LVD of the node is lower, the node is more important. Promising results of experiments on four real-world networks compared with six methods under both Susceptible–Infected (SI) model and Susceptible–Infected–Recovered (SIR) model validate and demonstrate the effectiveness of the proposed method.

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.

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