Yao Ding
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yao Ding has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Gan Luo
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China
1 shared publication - Hongtuo Qi
School of Civil Engineering, Chongqing University, Chongqing, China
1 shared publication - Jiepeng Liu
School of Civil Engineering, Chongqing University, Chongqing, China
1 shared publication - Xuhong Zhou
School of Civil Engineering, Chongqing University, Chongqing 400045, China;College of Civil Engineering, Hunan University, Changsha 410082, China
1 shared publication - Yi Xia
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China
1 shared publication - Yunzhu Liao
Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Ministry of Education, Chongqing 400045, China
1 shared publication
Latest Publications
- Open Access
- 18 April 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (8) , 4490
Automated Residential Bubble Diagram Generation Based on Dual-Branch Graph Neural Network and Variational Encoding
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.
- 18 April 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (8) , 4490
Open AccessAutomated Residential Bubble Diagram Generation Based on Dual-Branch Graph Neural Network and Variational Encoding
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.