From 1b7f396bc76b0b97039f63d2e4d15e5c33bddaa5 Mon Sep 17 00:00:00 2001
From: manxilin <matthewlin98@hotmail.com>
Date: Sun, 27 Dec 2020 23:51:22 +0100
Subject: [PATCH] update readme

---
 README.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 0bad42e..9e86a5c 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.
-- 
GitLab