While graph representation learning methods have been successfully applied to various graph data mining tasks, they typically couple graph information into an unstructured holistic representation. This makes it difficult to explicitly identify substructures with specific functionalities within the graph and lacks the ability to mine discriminative prototype structures that can be shared across graphs. To address these challenges, this paper proposes Prototype Subgraph Disentangled Graph Neural Network (PSDGNN). It explicitly disentangles node features into multiple independent latent factor groups through latent factor decomposition and prototype alignment mechanisms. A subgraph generator then converts these factors into factor subgraphs to model latent semantic substructures. Furthermore, learnable prototype subgraphs are introduced to represent foundational structural patterns shared across graphs. Through similarity matching and mutual information minimization objectives, the model aligns factor subgraphs with corresponding prototypes in structural semantics. Experimental results demonstrate superior performance over existing baseline methods across seven public datasets. The model provides intuitive, structured explanations for classification decisions through visualizable factor subgraphs and prototype subgraphs, significantly enhancing interpretability and generalization capabilities.
Yang et al. (Mon,) studied this question.