critical node identification
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2021 ◽  
Author(s):  
Manfu Ma ◽  
Xiangping Wang ◽  
Yong Li ◽  
Qiang Zhang ◽  
Changqing Wang

2020 ◽  
Vol 14 (14) ◽  
pp. 2771-2778
Author(s):  
Wen Zhang ◽  
Ke-yan Liu ◽  
Wanxing Sheng ◽  
Songhuai Du ◽  
Dongli Jia

2018 ◽  
Vol 246 ◽  
pp. 03023 ◽  
Author(s):  
Zhihui Wang ◽  
Zhang Ran ◽  
Xiangzhou Chen ◽  
Yundan Liang ◽  
Zhengdong Ren ◽  
...  

It has an important impact on the overall power communication network reliability to identify the location of critical nodes in the network and further develop protection methods. On the topology layer of power communication network, Node Topology Importance(NTI) is proposed based on calculating the rate of natural connectivity of power communication network after one node being destroyed; A calculation method of power business is presented by considering the power business requirements on the indexes, then taking into account the business layer and topology layer, weighted natural connectivity which fuses the node business information and topology information is defined, accordingly Node Combining Importance(NCI) is proposed to realize the excavation of key node. Finally, taking a real communication network as the simulating example, the simulation results by comparing with other methods show that the proposed method can improve the accuracy of recognition.


2016 ◽  
Vol 21 (19) ◽  
pp. 5621-5629 ◽  
Author(s):  
Fahong Yu ◽  
Xiaoyun Xia ◽  
Wenping Li ◽  
Jiang Tao ◽  
Longhua Ma ◽  
...  

2013 ◽  
Vol 12 (03) ◽  
pp. 469-489 ◽  
Author(s):  
ZHAOYANG ZHANG ◽  
HONGGANG WANG ◽  
KEN C. K. LEE ◽  
HUA FANG

This paper proposes a new approach that uses people's social interaction behavior collected by mobile phones and vital signs collected by wireless body area networks (WBAN) for epidemic control. By this approach, infectious people who are socially active can be quickly identified to be quarantined. To realize this approach, we introduce a notion of critical network and critical node identification algorithm. Observing some resource constraints such as quarantine cost and hardware limitation, we focus on optimizing the proposed approach such that high epidemic control effectiveness is achieved while the corresponding overhead is minimized. Our simulation results demonstrate that our approach can effectively control the spread of epidemic diseases in various situations.


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