用图相似性正则化改进Softmax，让GNN更好捕捉图结构信息。
Graph Similarity Regularized Softmax for Semi-Supervised Node Classification
- 在Softmax中引入非局部总变差正则化，融合图结构空间关系。
- 在引文和网页链接数据集上分类准确率提升，泛化能力更强。
- 适用于同质与异质图，适合做图神经网络节点分类的优化方案。
图神经网络（GNN）是针对图结构数据的强大深度学习模型，在诸多应用中表现优异。半监督节点分类中最常用的分类器是Softmax函数，但其缺乏对图结构的空间信息建模。本文提出一种图相似性正则化的Softmax方法，通过将非局部总变差（TV）正则化引入Softmax激活函数，更有效地捕获图中的内在空间信息。非局部梯度与散度算子的权重由图的邻接矩阵决定。我们将该方法应用于GCN和GraphSAGE架构，分别在引文数据集（Cora、Citeseer、Pubmed）和网页链接数据集（WebKB）上进行测试。数值实验表明，该方法在节点分类任务中表现良好，具备优秀的泛化能力。结果表明，该图相似性正则化Softmax在同质图和异质图上均有效。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) are powerful deep learning models designed for graph-structured data, demonstrating effectiveness across a wide range of applications.The softmax function is the most commonly used classifier for semi-supervised node classification. However, the softmax function lacks spatial information of the graph structure. In this paper, we propose a graph similarity regularized softmax for GNNs in semi-supervised node classification. By incorporating non-local total variation (TV) regularization into the softmax activation function, we can more effectively capture the spatial information inherent in graphs. The weights in the non-local gradient and divergence operators are determined based on the graph's adjacency matrix. We apply the proposed method into the architecture of GCN and GraphSAGE, testing them on citation and webpage linking datasets, respectively. Numerical experiments demonstrate its good performance in node classification and generalization capabilities. These results indicate that the graph similarity regularized softmax is effective on both assortative and disassortative graphs.
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