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 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 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.

scholarly journals EKSPLORASI PEMIMPIN OPINI UNTUK ALTERNATIF PENDUKUNG PEMASARAN PT. NET MEDIATAMA INDONESIA MENGGUNAKAN METODE ANALISIS JEJARING SOSIAL DI TWITTER

2019 ◽  
Vol 21 (1) ◽  
pp. 73-78
Author(s):  
Dian Puteri Ramadhani ◽  
Andry Alamsyah ◽  
Mukti Bawono Wicaksono
Keyword(s):  
Betweenness Centrality ◽  
Application Programming Interface ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Application Programming ◽  
Programming Interface

Pertumbuhan pesat teknologi di era globalisasi mengakibatkan pertukaran informasi tidak hanya terjadi pada dunia nyata. Internet telah menjadi kebutuhan pokok dalam menyebarkan informasi. Pertumbuhan penguna internet meningkatkan jumlah data yang beredar di seluruh dunia. Data interaksi yang pada media sosial dapat digunakan untuk melihat bagaimana suatu hal diperbincangkan. Penelitian ini bertujuan untuk menemukan aktor yang paling berperan dalam jaringan PT. Net Mediatama Indonesia di media sosial Twitter. Penelitian ini memanfaatkan sejumlah besar data yang diambil dari Twitter melalui Application Programming Interface. Data tersebut diteliti dengan pendekatan analisis jejaring sosial. Visualisasi dan perhitungan dilakukan menggunakan software Gephi. Aktor penting ditentukan berdasarkan degree centrality, closeness centrality, dan betweenness centrality. Pemain kunci dalam jaringan NET yaitu @chuuattac sehingga akun tersebut merupakan pemimpin opini yang pendapatnya didengarkan, dipercaya, dan membuat aktor lain bereaksi. Akun tersebut dapat digunakan sebagai alternatif pendukung pemasaran dalam mengkampanyekan produk dan menyebarkan informasi NET dengan lebih cepat dan tepat sasaran.

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