Identifying important nodes for temporal networks based on the ASAM model

Author(s):  
Jiu-lei Jiang ◽  
Hui Fang ◽  
Sheng-qing Li ◽  
Wei-min Li
2016 ◽  
Vol 4 (4) ◽  
pp. 446-459 ◽  
Author(s):  
SOUVIK SUR ◽  
NILOY GANGULY ◽  
ANIMESH MUKHERJEE

AbstractIn this paper, we attempt to investigate the attack tolerance of human mobility networks where the mobility is restricted to some extent, for instance, in a hospital, one is not allowed to access all locations. Similar situations also arise in schools. In such a network, we will show that people need to rely upon some intermediate agents, popularly known as the brokers to disseminate information. In order to establish this fact, we have followed the approach of attack in a network which in turn helps to identify important nodes in the network in order to maintain the overall connectivity. In this direction, we have proposed, a new temporal metric, brokerage frequency which significantly outperforms all other state-of-the-art attack strategies reported in Trajanovski et al. (2012), Sur et al. (2015).


1996 ◽  
Author(s):  
Eugene Santos ◽  
Young Jr. ◽  
Joel D.
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.


2021 ◽  
Vol 147 ◽  
pp. 110934
Author(s):  
Jialin Bi ◽  
Ji Jin ◽  
Cunquan Qu ◽  
Xiuxiu Zhan ◽  
Guanghui Wang ◽  
...  

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