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 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.

Identifying Important Packages of Object-Oriented Software Using Weighted k-Core Decomposition

2014 ◽  
Vol 23 (4) ◽  
pp. 461-476 ◽  
Author(s):  
Weifeng Pan ◽  
Bo Hu ◽  
Bo Jiang ◽  
Bo Xie
Keyword(s):  
Resource Allocation ◽  
Decomposition Method ◽  
Object Oriented ◽  
Closeness Centrality ◽  
The Other ◽  
Software Systems ◽  
Degree Centrality ◽  
Software Network ◽  
Influential Nodes ◽  
Better Than

AbstractIdentifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we introduce the weighted k-core decomposition method (Wk-core) to identify the important packages. First, we use a weighted software network to describe packages and their internal dependencies. Second, we use Wk-core to partition a software network into a layered structure. Then, the packages that are denoted by the nodes within the main core are the identified important packages. To evaluate our method, we use a variant of the susceptible–infectious–recovered model to examine the spreading influence of the nodes in six real weighted software networks. The results show that our method can well identify influential nodes, better than other four methods (i.e., original k-core decomposition, degree centrality, closeness centrality, and betweenness centrality methods). Furthermore, we demonstrate our method on two software networks and show that the important packages identified by our method are more meaningful from a software engineering perspective when compared with the other methods.

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.

Measuring Influence and Impact by Complex Network

2014 ◽  
Vol 551 ◽  
pp. 359-364
Author(s):  
Zheng Chang Zhang
Keyword(s):  
Complex Network ◽  
Betweenness Centrality ◽  
Relative Weight ◽  
Measurement Model ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Weighted Sum ◽  
Tennis Players ◽  
Influence Measurement ◽  
Set Up

This paper built two kinds of networks:co-author network and competition network and set up a system of influence measurement to determine who is most influential in the network.To evaluate the influence of co-authors, this paper introduced three norms: degree centrality, closeness centrality and betweenness centrality. Then, entropy value method was applied to get the relative weight of norms and establish co-author influence measurement model by the weighted sum of the three norms as influence marks. Meanwhile, the number of times players competed with each other among 10 tennis players in nearly 20 years was chosen to build our network. Because same as the co-author network, the competition network is undirected, we employ same algorithm to rank tennis players and analyze the first three players' competition relationship.

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 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 Dynamics of information diffusion and its applications on complex networks

2016 ◽  
Vol 651 ◽  
pp. 1-34 ◽  
Author(s):  
Zi-Ke Zhang ◽  
Chuang Liu ◽  
Xiu-Xiu Zhan ◽  
Xin Lu ◽  
Chu-Xu Zhang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Information Diffusion

scholarly journals A study on the influence of Korean Middle School students' relationship through Science class applying STAD cooperative learning

2018 ◽  
Vol 8 (4) ◽  
pp. 291 ◽  
Author(s):  
Dongryeul Kim
Keyword(s):  
Middle School ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Middle School Students ◽  
Collaborative Learning ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Science Class ◽  
School Students

  In order to find out the influence of Korean Middle School Students' relationship by science class applying STAD collaborative learning, this study conducted a social network analysis and sought to analyze the communication networks within the group and identified the change process of the type. The subject of this study was 30 students of the second grade at the girls' middle school located in Korea's Metropolitan City. For five weeks, science class applying STAD Collaborative Learning was implemented in the ‘reproduction and generation’ chapter. First, the class social network analysis showed that all the prices of density, degree centrality, closeness centrality, and betweenness centrality have risen after science class applying STAD Collaborative Learning. Also, the classroom's relationship index has improved. In other words, STAD Collaborative Learning encouraged interaction among students. Second, in order to research popularity, students' centrality analysis through the class social network analysis showed that top-ranked students' values of density, degree centrality, closeness centrality, and betweenness centrality appeared commonly high after science class applying STAD Collaborative Learning. Third, the analysis of the communication network change within six groups showed that all channel type appeared most often and circle type also appeared anew after science class applying STAD Collaborative Learning. In other words, it was possible to exchange information freely and communicate with all members of the group through STAD Collaborative Learning.

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