Leveraging Graph Neural Networks for Enhanced Node Representation Learning in Sparse Interaction Networks

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in learning node representations for graph-structured data. However, performance often degrades in scenarios characterized by sparse interactions and limited labeled data. This paper investigates strategies to mitigate the impact of sparsity on GNN performance. We explore techniques including graph augmentation, contrastive learning, and transfer learning to improve the quality of node embeddings learned from sparse interaction networks. Experimental results on synthetic and real-world datasets demonstrate the efficacy of our proposed approaches, highlighting the importance of addressing sparsity challenges in GNN applications.