TY - GEN
T1 - A Network Connectivity Reliability Estimation Model Based on Light Gradient Boosting Machine
AU - Chen, Bang
AU - Zhou, Shenghan
AU - Liu, Houxiang
AU - Ji, Xin Peng
AU - Zhang, Yue
AU - Chang, Wenbing
AU - Xiao, Yiyong
AU - Pan, Xing
N1 - Publisher Copyright:
© ESREL 2021. Published by Research Publishing, Singapore.
PY - 2021
Y1 - 2021
N2 - IoE (Internet of Everything) has become an inexorable trend of modern society development, which makes the network systems more and more complex. This also puts forward higher requirements for the security and reliability of complex network systems. Network connectivity reliability is a key index to evaluate network reliability. However, the computation complexity of the traditional exact algorithms increases exponentially with the expansion of network structure. Therefore, a network connectivity reliability estimation model based on LightGBM (Light Gradient Boosting Machine) is developed in this paper. The model takes the network structure sequence, link reliability, source node and target node as input and network connectivity reliability as output, which can realize the fast estimation of network connectivity reliability. A verification experiment is carried out on a data set of 81920 samples, which is obtain by the node traversal method and the inclusion-exclusion principle. The final experimental results also verify the effectiveness of the proposed model.
AB - IoE (Internet of Everything) has become an inexorable trend of modern society development, which makes the network systems more and more complex. This also puts forward higher requirements for the security and reliability of complex network systems. Network connectivity reliability is a key index to evaluate network reliability. However, the computation complexity of the traditional exact algorithms increases exponentially with the expansion of network structure. Therefore, a network connectivity reliability estimation model based on LightGBM (Light Gradient Boosting Machine) is developed in this paper. The model takes the network structure sequence, link reliability, source node and target node as input and network connectivity reliability as output, which can realize the fast estimation of network connectivity reliability. A verification experiment is carried out on a data set of 81920 samples, which is obtain by the node traversal method and the inclusion-exclusion principle. The final experimental results also verify the effectiveness of the proposed model.
KW - Approximation algorithms
KW - Lightgbm
KW - Machine learning
KW - Network connectivity reliability
UR - https://www.scopus.com/pages/publications/85135451246
U2 - 10.3850/978-981-18-2016-8_006-cd
DO - 10.3850/978-981-18-2016-8_006-cd
M3 - 会议稿件
AN - SCOPUS:85135451246
SN - 9789811820168
T3 - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
SP - 2136
EP - 2140
BT - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
A2 - Castanier, Bruno
A2 - Cepin, Marko
A2 - Bigaud, David
A2 - Berenguer, Christophe
PB - Research Publishing, Singapore
T2 - 31st European Safety and Reliability Conference, ESREL 2021
Y2 - 19 September 2021 through 23 September 2021
ER -