AI 中文总结
该研究针对共识伪标签学习中预测器证据同质化问题，通过修改类内结构构建互补决策源，提升了伪标签精度，明确源构建是该领域的关键设计问题。
AI 中文摘要
共识可提升伪标签学习的可靠性，但前提是其预测器能提供真正不同的证据；重复相同决策边界的多个模型会增加投票却不增加信息。我们通过对类内结构进行可控修改来构建决策源以解决该问题：从共享图表示出发，改变中心粒度与邻域混合方式，复现每个生成的源以测试其稳定性，再利用节点对共分配选择互补子集；随后对所选源的一致预测进行排序，用于学生模型训练。在Cora、CiteSeer和PubMed的公开固定划分上，以5个随机种子评估，与3个常规初始化的GCN源相比，所构建的源将固定预算训练的伪标签精度提升了1.19至4.39个百分点；在匹配的结构过滤条件下，3源共识在全部45个数据集槽比较中，精度均高于各组成源；增益在伪标签质量上最为显著，下游准确率仍具竞争力但未在所有数据集上领先。这些结果表明，源构建而非仅模型数量，是基于共识的伪标签学习的重要设计问题。
英文摘要
Consensus can make pseudo-label learning more reliable, but only when its predictors contribute genuinely different evidence. Multiple models that repeat the same boundary provide additional votes without additional information. We address this problem by con structing decision sources through controlled changes to within-class structure. Starting from a shared graph representation, we vary center granularity and neighborhood mixing, reproduce each resulting source to test its stability, and select a complementary subset using node pair coassignment. Unanimous predictions from the selected sources are then ranked for student training. On the public fixed splits of Cora, CiteSeer, and PubMed, evaluated with five random seeds, the constructed sources improve fixed-budget training pseudo-label precision by 1.19 to 4.39 percentage points over three conventionally initialized GCN sources. Under matched structural filters, three-source consensus is more precise than each constituent source in all 45 dataset slot eed comparisons. The gains are strongest in pseudo-label quality: downstream accuracy remains competitive but does not lead on every dataset. These results identify source construction rather than model count alone as an important design problem for consensus-based pseudo-label learning.
Comments14 pages, 4 figures, 6 tables
