Bubble diagrams containing key features and information are used for generative design of floor plans. The lack of reliable methods for automatically generating bubble diagrams significantly affects the smoothness of layout generation systems. To improve the time-consuming and unstable acquisition process, a novel method based on graph neural networks (GNNs) is proposed to generate various residential bubble diagrams. First, a dual-branch graph neural network (DBGNN) is introduced to learn the feature patterns of heterogeneous links, including connectivity and adjacency relations. Then, decentralized node sampling (DNS) and centralized node sampling (CNS) are proposed to enhance the local feature learning of DBGNN. Subsequently, a variational graph autoencoder (VGAE) is used to learn the implicit distribution of topological patterns, enabling the model to generate diverse outputs. Experimental results show that the proposed model performs excellently in two link prediction tasks, achieving 92.39% ACC-Door and 78.84% ACC-Wall, while also generating 50 distinct bubble diagrams, validating the effectiveness of the proposed method and demonstrating its outstanding application value.