论文标题
具有属性解码的可解释节点表示
Interpretable Node Representation with Attribute Decoding
论文作者
论文摘要
变分图自动编码器（VGAES）是从图形数据中无监督学习节点表示的强大模型。在这项工作中，我们系统地分析了VGAE中的建模节点属性，并证明属性解码对于节点表示学习很重要。我们进一步提出了一种新的学习模型，具有属性解码（NORAD）的可解释节点表示。该模型在可解释的方法中编码节点表示：节点表示在图中捕获社区结构以及社区和节点属性之间的关系。我们进一步提出了一个纠正程序，以完善孤立音符的节点表示形式，从而提高这些节点表示的质量。我们的经验结果证明了在可解释的方法学习图形数据时所提出的模型的优势。
Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we systematically analyze modeling node attributes in VGAEs and show that attribute decoding is important for node representation learning. We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD). The model encodes node representations in an interpretable approach: node representations capture community structures in the graph and the relationship between communities and node attributes. We further propose a rectifying procedure to refine node representations of isolated notes, improving the quality of these nodes' representations. Our empirical results demonstrate the advantage of the proposed model when learning graph data in an interpretable approach.
