Introduction The paper by Nelson Nauata et al. presents a novel graph-constrained generative adversarial network for a new house layout generation problem, whose task is to take an architectural constraint (a bubble diagram) as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. The paper also employ convolutional message passing neural networks (Conv-MPN), which differs from graph convolutional networks (GCNs). They argue that the architecture enables more effective higher-order reasoning for composing layouts and validating adjacency constraints. We aim to reproduce the results shown in the paper by using the existing code (making some changes as required to fit our dataset). First, we tried to replicate the results from the LIFULL HOME's dataset as used in the paper and then tried out four different methods to see if the results changed. Here are the alterations below describing our method: Remove the CMP layers, edges and room-type related features from the discriminator. Replacing the CMP layer with CNN and removing the edges and room-type features, which are required information in CMP. Since CMP is removed, this information will not be needed anymore. Changing sum-pooling to average pooling in the CMP layer. Changing the amount of neurons in particular networks.