# DIFF GRAPH U-NET: A DIFFERENTIAL GRAPH U-SHAPE NETWORK FOR GRAPH CLASSIFICATION
## Introduction
Regarding GCNs, there are mainly three tasks: node classification, link prediction, and graph classification. This project focuses on the classification of graphics through Differentiable Pooling (DIFFPOOL). DIFFPOOL is a state-of-the-art pool module that can learn the hierarchical expression of graphics and combine various end-to-end GCN structures. Hence, in order to research the changes of a graph after several DIFFPOOL modules respectively, the visualizations for pooling has been fulfilled via rebuilding the graph. Apart from DIFFPOOL, this project also explored the possibility to build an encoder-decoder architecture via building such a U-net-like method for graph data. A novel GCN architecture DIFF Graph U-Net is proposed.
## Author
Manxi Lin s192230
...
...
@@ -15,12 +18,15 @@ Unzip it in here
- See `main.ipynb`
- Proof of our result: `./screenshots`
## Our contribution
- Revision in `encoders.py` and `train.py` to implement DIFF Graph U-Net
- Implement `sample_data.py` to sample data sets
- Display our main result in a jupyter notebook `main.ipynb`
## DIFFPOOL
Repo Link https://github.com/RexYing/diffpool
## Introduction (from the origional repo)
This is the repo for Hierarchical Graph Representation Learning with Differentiable Pooling (NeurIPS 2018)
## DIFFPOOL Introduction (from the origional repo)
Recently, graph neural networks (GNNs) have revolutionized the field of graph
representation learning through effectively learned node embeddings, and achieved
state-of-the-art results in tasks such as node classification and link prediction.
