Identifying influential spreaders in complex networks based on network embedding and node local centrality

Author(s):  
Xu-Hua Yang ◽  
Zhen Xiong ◽  
Fangnan Ma ◽  
Xiaoze Chen ◽  
Zhongyuan Ruan ◽  
...  
Author(s):  
Dongxiao He ◽  
Youyou Wang ◽  
Jinxin Cao ◽  
Weiping Ding ◽  
Shizhan Chen ◽  
...  

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Zongning Wu ◽  
Zengru Di ◽  
Ying Fan

Network embedding is a frontier topic in current network science. The scale-free property of complex networks can emerge as a consequence of the exponential expansion of hyperbolic space. Some embedding models have recently been developed to explore hyperbolic geometric properties of complex networks—in particular, symmetric networks. Here, we propose a model for embedding directed networks into hyperbolic space. In accordance with the bipartite structure of directed networks and multiplex node information, the method replays the generation law of asymmetric networks in hyperbolic space, estimating the hyperbolic coordinates of each node in a directed network by the asymmetric popularity-similarity optimization method in the model. Additionally, the experiments in several real networks show that our embedding algorithm has stability and that the model enlarges the application scope of existing methods.


Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  

2013 ◽  
Vol 22 (2) ◽  
pp. 151-174 ◽  
Author(s):  
Richard Southwell ◽  
Jianwei Huang ◽  
Chris Cannings ◽  
◽  

Sign in / Sign up

Export Citation Format

Share Document