NodeMixup: Tackling Under-Reaching for Graph Neural Networks
DOI:
https://doi.org/10.1609/aaai.v38i13.29328Keywords:
ML: Graph-based Machine Learning, ML: Multi-class/Multi-label Learning & Extreme Classification, ML: Representation Learning, ML: Semi-Supervised LearningAbstract
Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are only accessible to a small portion of unlabeled nodes, leading to the under-reaching issue. In this study, we firstly reveal under-reaching by conducting an empirical investigation on various well-known graphs. Then, we demonstrate that under-reaching results in unsatisfactory distribution alignment between labeled and unlabeled nodes through systematic experimental analysis, significantly degrading GNNs' performance. To tackle under-reaching for GNNs, we propose an architecture-agnostic method dubbed NodeMixup. The fundamental idea is to (1) increase the reachability of labeled nodes by labeled-unlabeled pairs mixup, (2) leverage graph structures via fusing the neighbor connections of intra-class node pairs to improve performance gains of mixup, and (3) use neighbor label distribution similarity incorporating node degrees to determine sampling weights for node mixup. Extensive experiments demonstrate the efficacy of NodeMixup in assisting GNNs in handling under-reaching. The source code is available at https://github.com/WeigangLu/NodeMixup.Downloads
Published
2024-03-24
How to Cite
Lu, W., Guan, Z., Zhao, W., Yang, Y., & Jin, L. (2024). NodeMixup: Tackling Under-Reaching for Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14175-14183. https://doi.org/10.1609/aaai.v38i13.29328
Issue
Section
AAAI Technical Track on Machine Learning IV