User Classification of Social Networks Based on Feature Contrastive Learning and Graph Convolution
The user classification of social networks aims to determine the interests and hobbies of users through their personal attributes and social relations.This can be regarded as a node classification problem for graph data.Most node classification algorithms based on Graph Convolutional Neural Network(GCN)can handle datasets with high heterogeneity.However,social network datasets often exhibit high heterogeneity rate.This study proposes a feature Contrastive Learning-based GCN(CLGCN)model to alleviate this problem.A similarity matrix is constructed from the combined labels during the pretraining stage and used to perform graph convolution operation.Node pairs of features are defined as positive or negative sample pairs based on whether they belong to the same or different categories,respectively,using feature contrastive learning.Consequently,the representations of node pairs from the same category become more similar,whereas those of node pairs from different categories become more distinguishable by minimizing the loss function of feature contrastive learning.The experimental results on three low homogeneity rate social network datasets demonstrate that the accuracies of the proposed model for node classification are 93.5％,81.4％,and 67.9％,respectively,which are all better than those of the other comparative models.
social networkcontrastive learninghomogeneity rateGraph Convolutional Neural Network(GCN)node classification
万方数据
维普
