added pairnorm to readme

README.md

@@ -119,6 +119,7 @@ In detail, the following methods are currently implemented:
 * **[GraphSizeNorm](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.norm.GraphSizeNorm)** from Dwivedi *et al.*: [Benchmarking Graph Neural Networks](https://arxiv.org/abs/2003.00982) (CoRR 2020)
 * **[GNNExplainer](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.models.GNNExplainer)** from Ying *et al.*: [GNNExplainer: Generating Explanations for Graph Neural Networks](https://arxiv.org/abs/1903.03894) (NeurIPS 2019) [[**Example**](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/gnn_explainer.py)]
 * **[DropEdge](https://pytorch-geometric.readthedocs.io/en/latest/modules/utils.html#torch_geometric.utils.dropout_adj)** from Rong *et al.*: [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://openreview.net/forum?id=Hkx1qkrKPr) (ICLR 2020)
+* **[PairNorm](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.norm.PairNorm)** from Zhao and Akoglu: [PairNorm: Tackling Oversmoothing in GNNs](https://arxiv.org/abs/1909.12223) (ICLR 2020)
 * **[Tree Decomposition](https://pytorch-geometric.readthedocs.io/en/latest/modules/utils.html#torch_geometric.utils.tree_decomposition)** from Jin *et al.*: [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://arxiv.org/abs/1802.04364) (ICML 2018)
 
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