Stochastic Synchronization of Multiplex Networks with Continuous and Impulsive Couplings

Author(s):  
Xin Jin ◽  
Zhengxin Wang ◽  
Xiaojie Chen ◽  
Yang Cao ◽  
Guo-Ping Jiang
Author(s):  
Guanying Huo ◽  
Xin Jiang ◽  
Lili Ma ◽  
Quantong Guo ◽  
Yifang Ma ◽  
...  

2018 ◽  
Vol 499 ◽  
pp. 121-128 ◽  
Author(s):  
Wei Wang ◽  
Meng Cai ◽  
Muhua Zheng
Keyword(s):  

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Dana Vaknin ◽  
Bnaya Gross ◽  
Sergey V. Buldyrev ◽  
Shlomo Havlin

NeuroImage ◽  
2021 ◽  
Vol 229 ◽  
pp. 117738
Author(s):  
James C. Pang ◽  
Leonardo L. Gollo ◽  
James A. Roberts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.


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