基于异质图卷积注意网络的社交媒体账号分类

由于社交媒体网络的复杂性, 单一性质的同质信息网络对社交媒体账号分类会造成信息丢失, 对分类结果产生不利影响. 针对这种问题, 本文提出基于异质图卷积注意网络的社交媒体账号分类方法(HGCANA). 首先构建社交媒体的异质信息网络, 然后提取异质信息网络的社交媒体特征, 引入注意力机制, 对社交媒体账号进行分类识别. 通过实验比较HGCANA方法与现有方法, 证明了本文提出的HGCANA方法能够更好地对社交网络媒体账号进行有效分类.

Social Media Account Classification Based on Heterogeneous Graph Convolutional Attention Network

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.