通过多视图集成学习提升图神经网络的半监督分类性能
Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks
- 构建多个图增强视图，利用集成共识动态选择可信样本
- 自适应调整置信度阈值，减少误标传播与误差累积
- 适合处理噪声图结构，提升模型鲁棒性与泛化能力
本文提出一种新型框架，结合集成学习与增强图结构，提升图上半监督节点分类的性能与鲁棒性。通过创建同一图的多个增强视图，该方法利用‘多样化群体智慧’，缓解噪声图结构带来的挑战。集成学习使我们同时实现三个目标：基于模型一致性的自适应置信度阈值选择、训练中高置信度样本数量的动态确定，以及抗确认偏见的伪标签稳健提取。所提方法独特地融合自适应集成共识，灵活引导伪标签生成与样本选择，降低错误累积风险，提升鲁棒性。此外，基于集成共识的伪标签能捕捉个体模型常忽略的细微模式，增强模型泛化能力。在多个真实世界数据集上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple augmented views of the same graph, our approach harnesses the "wisdom of a diverse crowd", mitigating the challenges posed by noisy graph structures. Leveraging ensemble learning allows us to simultaneously achieve three key goals: adaptive confidence threshold selection based on model agreement, dynamic determination of the number of high-confidence samples for training, and robust extraction of pseudo-labels to mitigate confirmation bias. Our approach uniquely integrates adaptive ensemble consensus to flexibly guide pseudo-label extraction and sample selection, reducing the risks of error accumulation and improving robustness. Furthermore, the use of ensemble-driven consensus for pseudo-labeling captures subtle patterns that individual models often overlook, enabling the model to generalize better. Experiments on several real-world datasets demonstrate the effectiveness of our proposed method.
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