Learning Generalizable Contrastive Representations for Graph Zero-Shot Learning

This paper studies the problem of graph zero-shot learning, which aims at recognizing novel classes of nodes on the graph that are never seen during training. The key to graph zero-shot learning is establishing the mathematical relationship to transfer the prior knowledge of nodes from seen classes to unseen classes. However, the problem is largely under-explored and existing methods typically focus on acquiring supervision signals from seen classes or simply establishing connections between classes based solely on a semantic description matrix, such that the learned representations lack generalizable properties to unseen classes. To address this issue, this paper proposes GraphGCR that learns generalizable contrastive representations from the perspective of uniformity and alignment. Technically, GraphGCR leverages graph diffusion to extend supervised contrastive learning, encouraging the representations of semantics from different classes to be distributed uniformly and meanwhile achieve the alignment of node features and class semantics with the assistance of graph structural information. Moreover, to effectively enhance model generalizability, we further develop a class generator to synthesize features of unseen classes by embedding propagation and interpolation, thereby enriching the diversity of classes. Theoretical analysis also shows that our proposed framework exhibits strong discriminative property, which significantly enhances graph zero-shot learning. Experimental findings reveal that our GraphGCR achieves significant performance improvements over state-of-the-art methods across various benchmark datasets.