Self-training for graph class imbalance via global and local topology fusion

In real-world graph data, in addition to the class imbalance problem, there also exists topological imbalance. However, existing methods mainly focus on addressing class imbalance or only utilize shallow topological information. To address this issue, we first analyze the impact of graph robustness on label propagation and find that local topological robustness, measured by graph efficiency, plays a crucial role in label propagation. Based on this finding, we propose the GLoFT framework, which redefines topological weights by integrating global influence and local topological robustness, effectively addressing the topological imbalance problem. However, the implementation of GLoFT's core idea relies on high-quality labelled data generated by clustering. In partially labelled scenarios, a decline in pseudo-label quality can significantly weaken its effectiveness and even intensify the class imbalance problem. To cope with this challenge, we propose the fusion-based progressive matching for self-training module (FPMist), which is specifically designed for GLoFT, aiming to enhance the quality of pseudo-labels generated by clustering and improve GLoFT's robustness in partially labelled data settings. Experimental results demonstrate that GLoFT + FPMist outperforms existing methods constructed on multiple GNN backbones and in scenarios with severe class imbalance, showing its superior generalization ability and stability.