School of Computer Science and Engineering, Southeast University, Nanjing 211189, China;The 30th Research Institute of China Electronics Technology Group Corporation, Chengdu 610041, China 在期刊界中查找 在百度中查找 在本站中查找
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 在期刊界中查找 在百度中查找 在本站中查找
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 在期刊界中查找 在百度中查找 在本站中查找
Due to the complexity of social media networks, the classification of social media accounts by mono-nature homogeneous information networks causes information loss and has a negative impact on the classification results. To solve this problem, this study proposes a social media account classification method based on heterogeneous graph convolutional attention networks (HGCANA). Specifically, a heterogeneous information network of social media is constructed, and the social media features of the network are extracted. After that, the attention mechanism is introduced to classify and identify social media accounts. The HGCANA method is compared with the existing methods through experiments, and it is proved that the HGCANA method registers better performance in the effective classification of social media accounts.