Classifier-free Graph Few-shot Learning via Label-aware Clustering

Graph few-shot learning implies achieving node classification with extremely limited label information. To mitigate theperformance degradation of the graph neural network model under label scarcity, some previous work has attempted to facilitate the subsequent classification task with the aid of deep clustering. However, these methods merely treat this process as an intermediate step in providing prior knowledge, and do not fully exploit the potential of the clustering technique, which prevents the model from being trained in a more intuitive end-to-end manner. Therefore, we propose a novel graph few-shot learning framework via label-aware clustering (GFL-LC), benefiting from a tailored clustering process, our framework can obtain the final classification result without the classifier. Specifically, for the purpose of encouraging labeled nodes with the same class to learn similar feature representations that contribute to clustering them into the same cluster, we first engage label information in the graph representation learning process by defining an objective function. Next, on the basis of obtaining the preliminary label-aware clustering result, we draw on the idea of reinforcement learning to further improve its quality, which is capable of introducing optimization guidance by providing feedback from both the accuracy of labeled node division and structural rationality. Finally, considering that the ultimate task is a classification task, we design an alignment mechanism to process the clustering result, and transform it into the classification result. Comprehensive experiments on several benchmark datasets show that GFL-LC outperforms its peers, and confirms the superiority of our framework under the few-shot setting.