调优经典图神经网络，竟在多标签节点分类上超越复杂新模型。
Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?
- 用归一化、丢弃、残差连接等技巧优化传统GNN模型。
- 在五个数据集上，调优后的基线模型在四个上超越专门设计的方法。
- 适合关注模型调优与实验公平性的图学习研究者参考。
多标签节点分类（MLNC）近年依赖日益复杂的标签感知设计，显式建模节点-标签交互和标签间依赖关系。然而，这些方法的优势是否源于其特殊设计，还是仅仅因为基线模型未充分优化尚不明确。本文从强基线视角重新审视MLNC，探究精心调优的经典全图GNN是否足以成为有效解决方案。我们系统研究了GCN、SSGConv和GCNII等代表性骨干模型，并采用标准化但有效的技术如归一化、丢弃和残差连接进行优化。在五个代表性基准数据集上的实验表明，调优后的基线模型在四个数据集上表现优于代表性专用方法，并在多个设置中达到当前最优性能。结果表明，对经典骨干模型的细致调优是MLNC中极具影响力但常被忽视的因素，强调未来多标签图学习研究需开展更严格的强基线评估。
原文摘要 · Abstract (English)
Multi-label node classification (MLNC) has recently been addressed by increasingly complex label-aware designs that explicitly model node-label interactions and inter-label dependencies.However, it remains unclear whether the advantages of these methods truly stem from their specialized designs, or simply from insufficiently optimized baselines. In this paper, we revisit MLNC from a strong-baseline perspective and investigate whether carefully tuned classic full-graph GNNs can already serve as strong solutions to this task. We systematically study several representative backbones, including GCN, SSGConv, and GCNII, and optimize them using standard yet effective techniques such as normalization, dropout, and residual connections. Experiments on five representative benchmark datasets show that our tuned baselines outperform representative specialized methods on four datasets and achieve state-of-the-art performance in multiple settings. These results indicate that careful tuning of classic backbones is a highly influential but often overlooked factor in MLNC, and highlight the need for more rigorous strong-baseline evaluation in future research on multi-label graph learning.
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