## 1 Introduction  
The architectural floor plan is a crucial component of architectural design, reflecting in detail the functional requirements of the building and the relationships between its spaces [1]. Traditional floor plan design typically relies on experienced architects, which can be time-consuming and may lead to variations in efficiency [2]. With advancements in artificial intelligence, neural network-based floor plan generation methods have shown potential in improving design efficiency, enhancing and assisting traditional labor-intensive approaches [3]. These floor plan generation models are based on sparse design inputs, such as bubble diagrams and area constraints, to automatically create the desired layout [4, 5]. For these generation models, the bubble diagram plays a key role, as it effectively represents the functional spaces of the floor plan while also describing the organizational relationships between different functional areas [6]. Floor plan generation models progressively reason from the information conveyed by the bubble diagram to achieve a topologically consistent layout. Therefore, the accuracy and structure of the bubble diagram directly influence the feasibility and effectiveness of the generated layout. However, ensuring the diversity and reliability of bubble diagrams in current floor plan generation models remains challenging. These bubble diagrams are either extracted from floor plan datasets [7], or manually designed by users [8]. For example, [PERSON] et al. [9] obtained bubble diagrams from floor plan datasets and used a generative adversarial network (GAN) to map these bubble diagrams to layouts. [PERSON] et al. [10] extracted bubble diagrams from the RPLAN dataset and used a diffusion model to generate floor plans. [PERSON] et al. [11] pre-builds a bubble diagram database and retrieves bubble diagrams based on user-provided boundaries, which are then input into GNN and convolutional neural networks (CNNs) to generate layouts. IPLAN [12] allows users to interactively design bubble diagrams and progressively generate layouts using a variational autoencoder (VAE). Nevertheless, these bubble diagram acquisition strategies may limit the application of floor plan generation models in real-world design scenarios. On the one hand, extracting bubble diagrams from datasets does not create new bubble diagrams, and the styles of the obtained bubble diagrams are limited, which may hinder the generation of innovative solutions and lead to challenges in layout diversity [13]. On the other hand, bubble diagrams designed by non-professional users may contain errors, and the model may fail to satisfy valid topological constraints during the generation process, leading to issues such as spatial conflicts or illogical functionality in the generated floor plans. Additionally, the extra steps of manually creating or retrieving bubble diagrams may reduce the usability of software [14]. Therefore, there is a need to propose a method capable of automatically generating reliable bubble diagrams. However, automatically creating reliable bubble diagrams is challenging, particularly due to the complexity of the topological structures. A bubble diagram is an abstract schematic of the architectural floor plan, describing the functional attributes and spatial organization of the building [15]. In a bubble diagram, there are links between various functional nodes that represent spatial relationships, including connectivity and adjacency [16]. The ways in which these functional nodes are connected are numerous, and even between nodes of the same type, there are various forms of connectivity or adjacency. Additionally, the degree of each functional node must be controlled within a reasonable range to avoid incorrect topological structures [17]. Furthermore, due to the dynamic nature of the link forms and quantities, the bubble diagram topology created with the same nodes can vary greatly. These characteristics present fundamental requirements for bubble diagram generation models.