Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?

2026-05-02

AI Summary

This work investigates whether the reported performance gains of existing multi-label node classification methods stem from specialized designs or merely from insufficient optimization of classical baselines. To address this, the authors systematically enhance general-purpose GNN architectures—such as GCN, SSGConv, and GCNII—by integrating standard techniques including normalization, Dropout, and residual connections to construct strong baselines. Extensive experiments demonstrate that these well-tuned classical models outperform current specialized approaches on four out of five benchmark datasets and achieve state-of-the-art results across various experimental settings. These findings underscore the critical importance of employing rigorously optimized baselines in multi-label graph learning research to ensure meaningful methodological comparisons.

Abstract

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.

Problem

• multi-label node classification
• graph neural networks
• strong baselines
• label dependencies
• node-label interactions

Innovation

• multi-label node classification
• graph neural networks
• strong baselines
• model tuning
• GNN optimization