Learning Node Representations Under Partial Label Learning

Jiaguo Yuan, Hang Gao, Fengge Wu, Junsuo Zhao

Abstract: Node classification is a crucial area in graph representation learning, with significant applications in real-world network scenarios. However, due to the complexity of the relationships among nodes in the topological graph, precise data labeling is often challenging, leading to a significant amount of label noise. Partial Label Learning (PLL) is a weakly supervised learning problem designed to accommodate label noise. Therefore, we introduce PLL to address the issue of label noise. Currently, Graph Neural Networks (GNNs) are the primary method for addressing node classification problems. However, existing research has demonstrated that GNNs tend to amplify the similarity between node features, posing challenges for label disambiguation in partial label scenarios. To address this issue, We conduct an analysis of the composition of node features and propose a novel method, which aims to enhance feature quality and reduce node feature similarity in partial label scenarios of node classification. Extensive experiments on challenging homogeneous graph datasets indicate that PLNR achieves state-of-the-art performance and demonstrates comparable results to fully supervised learning.