GCNTransformer

Code for paper Housing Price Prediction using Graph Convolutional Transformer Network

Running

Data Preprocessing

python Sampling.py to sample from the data, we need to specify :

• How many days is in one time window,
• How many nodes we would like to sample per time window.

python main.py to build adjacency matrix, similarity matrix, feature matrix, label nodes.

Model Training

python baseline.py, to train using Regression.

python gcn_only.py, using only 2 GCN layers.

python gcn_lstm.py, 2 GCN layers + LSTM layer.
We need to specify :

• Number of nodes per time window (--house size)

python gcn_transformer_original.py, 2 GCN layers with Transformer encoder + sinusoidal positional embedding¹.
We need to specify:

• Number of nodes per time window
• Feed forward dimension

python gcn_transformer.py, 2 GCN layers with Transformer encoder + Time2Vec² as time embedding.
We need to specify:

• Number of nodes per time window
• Feed forward dimension

Footnotes

1. From the paper Attention is all you need ↩

2. From the paper Time2Vec: Learning a Vector Representation of Time ↩