Dynamic Fuzzy Sampler for Graph Neural Networks
Jia Wei, Xingjun Zhang, Witold Pedrycz, Weiping Ding · IEEE Transactions on Fuzzy Systems · 2025
Graph neural networks (GNNs) have been widely used in many fields. Inductive learning has replaced transductive learning as the current mainstream paradigm for GNN training due to its higher memory efficiency, computing speed, and stronger generalization. Neighbor node sampling as a key step in GNN inductive learning is crucial to the model performance. However, existing samplers only focus on how to sample nodes from the adjacency matrix, ignoring the fact that different neighbors have different impacts on the target node at different moments. They usually aggregate the neighbor information in a simple way such as averaging or summing, which limits the information representation, robustness, and generalization. In order to address the shortcomings of existing graph inductive learning samplers, this article proposes a dynamic fuzzy sampler (DFS) based on a Gaussian fuzzy system. The DFS fully takes into account the diversity of nodes in the graph-structured data, and efficiently models and handles the uncertainties and fuzziness of the mutual information of various nodes at different moments. Specifically, DFS first innovatively constructs a learnable Gaussian fuzzy set system for determining the membership degree of different neighbors to the target node at different moments. Subsequently, DFS aggregates the target node embeddings and membership-weighted neighbor embeddings to update the target node's features, which makes the target node utilize the sampling information more effectively. The aggregated target node effectively captures the graph structure information and neighbor node information, which can facilitate the subsequent GNN-based graph representation model with stronger representation and generalization capabilities. Our supervised and self-supervised experimental results on graph datasets of different sizes show that DFS has consistently excellent performance, significantly outperforming other state-of-the-art sampling schemes. DFS achieves up to 1.90% and 9.52% F1-score improvement compared to the state-of-the-art schemes on small- and large-scale graphs, respectively.