Volume 29 Issue 6
Dec.  2020
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FENG Yong, GAI Ming, WANG Fuhai, WANG Rongbing, XU Xiaowei. Classification and Early Warning Model of Terrorist Attacks Based on Optimal GCN[J]. Chinese Journal of Electronics, 2020, 29(6): 1193-1200. doi: 10.1049/cje.2020.10.005
Citation: FENG Yong, GAI Ming, WANG Fuhai, WANG Rongbing, XU Xiaowei. Classification and Early Warning Model of Terrorist Attacks Based on Optimal GCN[J]. Chinese Journal of Electronics, 2020, 29(6): 1193-1200. doi: 10.1049/cje.2020.10.005

Classification and Early Warning Model of Terrorist Attacks Based on Optimal GCN

doi: 10.1049/cje.2020.10.005
Funds:  This work is supported by the National Nature Science Foundation of China under Grant No.71771110, the Social science planning foundation of Liaoning province of China under Grant No. L18AGL007, the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University under Grant No.93K172018K01, the project of Liaoning Provincial Engineering Laboratory of Big Data System of Public Opinion and Network Security under Grant No.2016-294, the Foundation of China Scholarship Council.
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  • Corresponding author: WANG Rongbing (corresponding author) was born in1979. He received the Ph.D. degree in business management from Liaoning University. He is an associate professor at Liaoning University. He is a visiting scholar of Edinburgh University. His research interests include data mining, cloud computing and machine learning. (Email:wrb@lnu.edu.cn)
  • Received Date: 2019-07-19
  • Publish Date: 2020-12-25
  • Aiming at the lack of effective quantitative model to support the analysis of terrorist attacks, a multilayer depth Neural network (NN) Graph convolutional networks (GCN) model (NNGCN) was put forward to realize the classification and early warning of terrorist attacks. The proposed model optimized the traditional GCN with the help of complex NN. The concept of link index was introduced into the NNGCN model. It is combined with the important information between event nodes. The information includes the similarity of events and link probability. Compared with the original unoptimized model, the improved model increased the classification accuracy of terrorist attacks. Because the model uses the node's feature information and the link relationship of graph structure, it can also warn the sudden terrorist attacks effectively.
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