Abstract:

Existing methods used for academic paper classification neglect relational information. To address this issue, this paper proposes a novel classification model that integrates a Graph Convolutional Network (GCN) with contrastive learning, called a Contrastive Graph Convolutional Network (CGCN). First, two distinct types of homogeneous-heterogeneous relational information are defined based on the content and citations of papers, and they are transformed into self-supervised information to construct contrastive loss. Second, contrastive loss is employed to enhance the feature extraction process of the GCN, such that homogeneous papers are extracted to be close to one another, while heterogeneous papers remain distant. Third, cross-entropy loss and the Softmax function are utilized to complete end-to-end academic paper classification. When tested on three benchmark academic datasets, the CGCN outperforms advanced baselines in the classification task. Its Micro-F1 and Macro-F1 scores are higher than those of the original GCN by 8.29 and 7.91 percentage points, respectively, on the Cora dataset. The CGCN enhances the capacity to represent potential information in papers by employing a contrastive loss based on a homogeneous—heterogeneous relationship, thereby improving the prediction accuracy and generalization. This method provides innovative ideas and methods for academic paper classification.

Key words: paper classification, Graph Convolutional Network (GCN), contrastive learning, homogeneous relationship, heterogeneous relationship

摘要：

针对现有学术文献分类方法忽略文献数据之间关联信息的问题, 提出一种融合图卷积网络(GCN)和对比学习的文献分类模型对比图卷积网络(CGCN)。首先, 基于文献内容和引用关系定义两类"同质-异质"关联信息, 并将其转换为构建对比损失的自监督信息; 然后, 利用对比损失优化GCN特征提取过程, 推动同质文献特征表示彼此接近、异质文献特征彼此远离; 最后, 利用交叉熵损失和Softmax函数实现"端到端"的学术文献分类。在3个基准学术文献数据集上, CGCN的文献分类表现优于当前较为先进的基线模型, 特别是Cora数据集上Micro-F1和Macro-F1指标值相较原始的GCN模型分别提高8.29和7.91百分点。CGCN通过基于"同质-异质"关系构建的对比损失, 增强了模型对文献数据潜在信息的表征能力, 提高了分类的准确性和泛化性, 为学术文献分类研究提供了新思路和新方法。

关键词: 文献分类, 图卷积网络, 对比学习, 同质关系, 异质关系