TY - GEN
T1 - Contrastive Meta-Learning for Few-shot Node Classification
AU - Wang, Song
AU - Tan, Zhen
AU - Liu, Huan
AU - Li, Jundong
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/8/6
Y1 - 2023/8/6
N2 - Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes for classification via a novel similarity-sensitive mix-up strategy. Extensive experiments on prevalent few-shot node classification datasets verify the effectiveness of our framework and demonstrate its superiority over other state-of-the-art baselines.
AB - Few-shot node classification, which aims to predict labels for nodes on graphs with only limited labeled nodes as references, is of great significance in real-world graph mining tasks. To tackle such a label shortage issue, existing works generally leverage the meta-learning framework, which utilizes a number of episodes to extract transferable knowledge from classes with abundant labeled nodes and generalizes the knowledge to other classes with limited labeled nodes. In essence, the primary aim of few-shot node classification is to learn node embeddings that are generalizable across different classes. To accomplish this, the GNN encoder must be able to distinguish node embeddings between different classes, while also aligning embeddings for nodes in the same class. Thus, in this work, we propose to consider both the intra-class and inter-class generalizability of the model. We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs. First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes. Second, we strengthen the inter-class generalizability by generating hard node classes for classification via a novel similarity-sensitive mix-up strategy. Extensive experiments on prevalent few-shot node classification datasets verify the effectiveness of our framework and demonstrate its superiority over other state-of-the-art baselines.
KW - few-shot learning
KW - graph neural networks
KW - weak supervision
UR - http://www.scopus.com/inward/record.url?scp=85171339316&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171339316&partnerID=8YFLogxK
U2 - 10.1145/3580305.3599288
DO - 10.1145/3580305.3599288
M3 - Conference contribution
AN - SCOPUS:85171339316
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2386
EP - 2397
BT - KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
Y2 - 6 August 2023 through 10 August 2023
ER -