Predicting Emerging Trends of Keywords based on Graph Neural Network

doi:10.23940/ijpe.20.12.p12.19571964

Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (12): 1957-1964.doi: 10.23940/ijpe.20.12.p12.19571964

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Predicting Emerging Trends of Keywords based on Graph Neural Network

Jie Yin^{a, b, c, d}, Jiayin Liu^{a, c, d, *}, Min Yuan^{a, c, d}

^aDepartment of Network Security Corps, Jiangsu Police Institute, Nanjing, 210031, China;
^bState Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China;
^cJiangsu Electronic Data Forensics and Analysis Engineering Research Center, Jiangsu Police Institute, Nanjing, 210031, China;
^dJiangsu Provincial Public Security Department Key Lab of Digital Forensics, Jiangsu Police Institute, Nanjing, 210031, China

Submitted on ; Revised on ; Accepted on
Contact: * Corresponding author. E-mail address: liujiayin@jspi.cn
Supported by:
This research has been supported by the National Natural Science Foundation of China (Grant No. 61802155).

Abstract

Abstract: In order to achieve better prediction results on emerging trends of keywords, a graph neural network is used to mine the relationship between keywords. This paper first constructs a keyword network based on co-occurrence relationship. Then, a feature template is carefully designed. Finally, the relationships among keywords are captured by a graph neural network, and the method for predicting emerging trends is proposed. The results on the test set show that the average Euclidean distance of the prediction results of this method is 12.33, and the rank correlation is 0.85, which significantly outperforms the baseline methods.

Key words: emerging trends prediction, network, graph neural network

Jie Yin, Jiayin Liu, Min Yuan. Predicting Emerging Trends of Keywords based on Graph Neural Network [J]. Int J Performability Eng, 2020, 16(12): 1957-1964.

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Predicting Emerging Trends of Keywords based on Graph Neural Network

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