diff --git a/README.md b/README.md index 9e86a5c4c2c03fbf7173f4a4cab3f1af8ffc578b..303f5bc69afc897670e7dcef3ffc4ad0100ca419 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# DIFF GRAPH U-NET: A DIFFERENTIAL GRAPH U-SHAPE NETWORK FOR GRAPH CLASSIFICATION +# DIFF GRAPH U-NET: A DIFFERENTIAL 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. @@ -44,4 +44,4 @@ on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets. -Paper link: https://arxiv.org/pdf/1806.08804.pdf \ No newline at end of file +Paper link: https://arxiv.org/pdf/1806.08804.pdf