Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes and labels' semantics thus enabling knowledge transfer from seen to unseen classes. However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i.e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels. It's necessary to separate and judge the semantic factors that tremendously affect the cognitive ability to improve the generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics via the extracted KG (Knowledge Graph)-based topics. And then the content of each node is reconstructed to a topic-level representation that offers multi-faceted and fine-grained semantic relevancy to different labels. Due to the particularity of the graph's instance (i.e., node) representation, a novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation. Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines.

翻译：近年来，零样本节点分类（ZNC）已成为图数据分析中新兴且关键的任务。该任务旨在预测训练过程中未见类别的节点。现有工作主要利用图神经网络（GNNs）来关联特征原型与标签语义，从而实现从已知类别到未知类别的知识迁移。然而，以往工作忽略了特征-语义对齐中的多面语义导向问题，即一个节点的内容通常涵盖与多个标签语义相关的多种主题。为了提升模型的泛化能力，有必要分离并判断对认知能力产生重大影响的语义因素。为此，我们提出了一种知识感知的多面框架（KMF），该框架通过提取基于知识图谱（KG）的主题来增强标签语义的丰富性。随后，每个节点的内容被重构为主题级表示，以提供不同标签的多面且细粒度的语义相关性。鉴于图实例（即节点）表示的特殊性，我们开发了一种新颖的几何约束，以缓解由节点信息聚合引起的原型漂移问题。最后，我们在多个公开图数据集上进行了广泛实验，并设计了零样本跨域推荐的应用场景。定量结果表明，与当前最先进的基线方法相比，KMF兼具有效性和泛化能力。