KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot
  Node Classification
Likang Wu Junji Jiang Hongke Zhao Hao Wang Defu Lian Mengdi Zhang Enhong Chen
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.