diff --git a/README.md b/README.md index 0bad42e79bc841e44a3bc489cbf24b1b4893493c..9e86a5c4c2c03fbf7173f4a4cab3f1af8ffc578b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,7 @@ # 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.