LLM-Empowered Few-Shot Node Classification on Incomplete Graphs with Real Node Degrees

Yun Li, Yi Yang, Jiaqi Zhu, Hui Chen, Hongan Wang

Abstract: Graphs constructed from real-world scenarios are often incomplete due to privacy restrictions or resource limitations, posing significant challenges for node classification, especially when labeled data are scarce. In many scenarios of incomplete graphs, the real node degrees, such as the number of followers in social networks or publications' references in citation networks, are easily accessible and informative, which could indicate the degree of incompleteness. However, most of existing researches of incomplete graphs focus on edge completion, but ignore the node completion with known node degrees. In this paper, we propose a new few-shot node classification problem on incomplete graphs with real node degrees. To deal with node completion, edge completion and label completion of this problem, we develop an effective Large Language Models (LLMs) empowered Graph Convolutional Network (GCN) model utilizing the real node Degrees, namely LLMDGCN. First, we leverage LLMs to initially fill in the missing nodes and labels. Next, we design an edge prediction module that employs the real node degrees and inter-category probability matrix to recover the missing edges for each node. We then iteratively train the GCN and the edge prediction module. The GCN generates pseudo labels, which the edge prediction module uses to restore edges, and these edges are fed back into the GCN to improve accuracy. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of our proposed method for the few-shot node classification on incomplete graphs with real node degrees.