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README.md

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 ## Check our main result
 - See `main.ipynb`
 - Proof of our result: `./screenshots`
+
+# 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)
+
+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.
+However, current GNN methods are inherently flat and do not learn hierarchical
+representations of graphs—a limitation that is especially problematic for the task
+of graph classification, where the goal is to predict the label associated with an
+entire graph. Here we propose DIFFPOOL, a differentiable graph pooling module
+that can generate hierarchical representations of graphs and can be combined with
+various graph neural network architectures in an end-to-end fashion. DIFFPOOL
+learns a differentiable soft cluster assignment for nodes at each layer of a deep
+GNN, mapping nodes to a set of clusters, which then form the coarsened input
+for the next GNN layer. Our experimental results show that combining existing
+GNN methods with DIFFPOOL yields an average improvement of 5–10% accuracy
+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
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