Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

2021 ◽  
Author(s):  
Sanjay Kumar ◽  
Ankit Panda
Keyword(s):  
Complex Networks ◽  
Influential Nodes ◽  
Weighted Complex Networks

scholarly journals Identifying Influential Nodes in Complex Networks Based on Local and Global Methods

2021 ◽  
Vol 1738 ◽  
pp. 012026
Author(s):  
Li Mijia ◽  
Wei Hongquan ◽  
Li Yingle ◽  
Liu Shuxin
Keyword(s):  
Complex Networks ◽  
Influential Nodes

Information mining in weighted complex networks with nonlinear rating projection

2017 ◽  
Vol 51 ◽  
pp. 115-123 ◽  
Author(s):  
Hao Liao ◽  
An Zeng ◽  
Mingyang Zhou ◽  
Rui Mao ◽  
Bing-Hong Wang
Keyword(s):  
Complex Networks ◽  
Information Mining ◽  
Weighted Complex Networks

Finding the Most Influential Nodes in Pinning Controllability of Complex Networks

2017 ◽  
Vol 64 (6) ◽  
pp. 685-689 ◽  
Author(s):  
Ali Moradi Amani ◽  
Mahdi Jalili ◽  
Xinghuo Yu ◽  
Lewi Stone
Keyword(s):  
Complex Networks ◽  
Influential Nodes

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.

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