Abstract
The success of GNNs highly relies on the accurate labeling of data. Existing methods of ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing nodes in the graphs. However, in reality, new nodes always continuously emerge on dynamic graphs, with different categories and even label noises. To this end, we formulate a new problem, Graph Cross-Supervised Learning, or Graph Weak-Shot Learning, that describes the challenges of modeling new nodes with novel classes and potential label noises. To solve this problem, we propose Lipshitz-regularized Mixture-of-Experts similarity network (LIME), a novel framework to encode new nodes and handle label noises. Specifically, we first design a node similarity network to capture the knowledge from the original classes, aiming to obtain insights for the emerging novel classes. Then, to enhance the similarity network's generalization to new nodes that could have a distribution shift, we employ the Mixture-of-Experts technique to increase the generalization of knowledge learned by the similarity network. To further avoid losing generalization ability during training, we introduce the Lipschitz bound to stabilize model output and alleviate the distribution shift issue. Empirical experiments validate LIME's effectiveness: we observe a substantial enhancement of up to 11.34% in node classification accuracy compared to the backbone model when subjected to the challenges of label noise on novel classes across five benchmark datasets. The code can be accessed through https://github.com/xiangchi-yuan/Graph-Cross-Supervised-Learning.