Few-shot Graph Classification with Contrastive Loss and Meta-classifier

Abstract

Few-shot graph-level classification based on graph neural networks is critical in many tasks including drug and material discovery. We present a novel graph contrastive relation network (GCRNet) by introducing a practical yet straightforward graph meta-baseline with contrastive loss to gain robust representation and meta-classifier to realize more suitable similarity metric, which is more adaptive for graph few-shot problems. Experimental results demonstrate that the proposed method achieves 8%-12% in 5-shot, 5%-8% in 10 shot, and 1%-5% in 20-shot improvements, respectively, compared to the existing state-of-the-art methods.