Adversarial Regularization for Enhanced Generalization in Graph Neural Networks with Sparse Adjacency Matrices

Graph Neural Networks (GNNs) have demonstrated remarkable performance across various domains. However, their efficacy often diminishes when dealing with graphs characterized by sparse adjacency matrices, a common occurrence in real-world scenarios where complete relational information is unavailable. This paper proposes an adversarial regularization framework to improve the generalization capabilities of GNNs trained on sparse graphs. We introduce an adversarial component that perturbs the node embeddings during training, forcing the GNN to learn more robust representations insensitive to minor variations in the input graph structure. Our experiments on benchmark datasets demonstrate that the proposed approach significantly enhances the performance of GNNs, particularly in scenarios with high sparsity, mitigating overfitting and improving out-of-sample generalization.