Abstract

Graph Neural Networks (GNNs) have achieved promising performance in various graph learning tasks. Real-world graph learning often suffers from severe label scarcity, which hinders GNNs’ feature and topology learning and causes poor generalization. Node mixup has therefore been shown to be an effective graph data augmentation paradigm for enhancing GNNs’ performance in label-scarce graph learning scenarios. However, mainstream node mixup methods adopt fixed or randomly sampled mixing ratios while neglecting inherent correlations between node pairs, thereby producing less informative or noisy augmented samples. To address the issue, we propose a novel semantic-aware adaptive node mixup method, SA-Mixup, for node classification tasks. Specifically, SA-Mixup first leverages high-confidence pseudo-labeling to enrich supervisory signals by incorporating reliable unlabeled nodes into the training set, and then it selects node pairs with the same label for node mixup. Furthermore, an adaptive node mixup mechanism is developed to dynamically learn optimal mixing ratios according to semantic relation and predictive uncertainty for each node pair. In addition, a similarity-guided neighbor connection strategy is introduced to select candidate neighbors for generated virtual nodes and establish reasonable topological connections. The experimental results show that the proposed method achieves strong performance when combined with different GNN backbones. The ablation results further show that both the adaptive node mixup mechanism and the similarity-guided neighbor connection contribute to the performance improvements.

IPC Classification

Keywords