Abstract:

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.

Key words: social network, contrastive learning, homogeneity rate, Graph Convolutional Neural Network(GCN), node classification

摘要：

社交网络用户分类旨在通过用户属性和社交关系确定用户的兴趣爱好, 可通过图类数据的节点分类实现。多数基于图卷积神经网络(GCN)的节点分类方法仅能处理高同质率数据集, 但社交网络数据集通常具有较高的异质率。针对社交网络数据集同质率较低的问题, 提出一种基于特征对比学习的图卷积神经网络(CLGCN)模型。通过预训练的组合标签构造相似性矩阵, 根据相似性矩阵进行图卷积。利用特征对比学习分别定义类别相同和不同的邻居节点对为正负样本对, 最小化特征对比的损失函数, 使同类节点对的特征表达相似性更高及异类节点对的特征表达可区分性更强。实验结果表明, CLGCN模型在3个低同质率社交网络数据集上的节点分类准确率分别达到93.5%、81.4%和67.9%, 均高于对比模型。

关键词: 社交网络, 对比学习, 同质率, 图卷积神经网络, 节点分类