Fix typos in MinCutPooling (#4895)

* update

* changelog

Author: rusty1s

CHANGELOG.md

@@ -68,7 +68,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Refactored reading molecular positions from sdf file for qm9 datasets ([4654](https://github.com/pyg-team/pytorch_geometric/pull/4654))
 - Fixed `MLP.jittable()` bug in case `return_emb=True` ([#4645](https://github.com/pyg-team/pytorch_geometric/pull/4645), [#4648](https://github.com/pyg-team/pytorch_geometric/pull/4648))
 - The generated node features of `StochasticBlockModelDataset` are now ordered with respect to their labels ([#4617](https://github.com/pyg-team/pytorch_geometric/pull/4617))
-- Fixed typos in the documentation ([#4616](https://github.com/pyg-team/pytorch_geometric/pull/4616), [#4824](https://github.com/pyg-team/pytorch_geometric/pull/4824))
+- Fixed typos in the documentation ([#4616](https://github.com/pyg-team/pytorch_geometric/pull/4616), [#4824](https://github.com/pyg-team/pytorch_geometric/pull/4824), [#4895](https://github.com/pyg-team/pytorch_geometric/pull/4895))
 - The `bias` argument in `TAGConv` is now actually applied ([#4597](https://github.com/pyg-team/pytorch_geometric/pull/4597))
 - Fixed subclass behaviour of `process` and `download` in `Datsaet` ([#4586](https://github.com/pyg-team/pytorch_geometric/pull/4586))
 - Fixed filtering of attributes for loaders in case `__cat_dim__ != 0` ([#4629](https://github.com/pyg-team/pytorch_geometric/pull/4629))

README.md

@@ -256,7 +256,7 @@ It is commonly applied to graph-level tasks, which require combining node featur
 * **[GlobalAttention](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.glob.GlobalAttention)** from Li *et al.*: [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493) (ICLR 2016) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/global_attention.py)]
 * **[Set2Set](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.aggr.Set2Set)** from Vinyals *et al.*: [Order Matters: Sequence to Sequence for Sets](https://arxiv.org/abs/1511.06391) (ICLR 2016) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/set2set.py)]
 * **[Sort Pool](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.glob.global_sort_pool)** from Zhang *et al.*: [An End-to-End Deep Learning Architecture for Graph Classification](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf) (AAAI 2018) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/benchmark/kernel/sort_pool.py)]
-* **[MinCUT Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.mincut_pool.dense_mincut_pool)** from Bianchi *et al.*: [MinCUT Pooling in Graph Neural Networks](https://arxiv.org/abs/1907.00481) (CoRR 2019) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_mincut_pool.py)]
+* **[MinCut Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.mincut_pool.dense_mincut_pool)** from Bianchi *et al.*: [Spectral Clustering with Graph Neural Networks for Graph Pooling](https://arxiv.org/abs/1907.00481) (ICML 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_mincut_pool.py)]
 * **[DMoN Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.dense.dmon_pool.DMoNPooling)** from Tsitsulin *et al.*: [Graph Clustering with Graph Neural Networks](https://arxiv.org/abs/2006.16904) (CoRR 2020) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/proteins_dmon_pool.py)]
 * **[Graclus Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.graclus)** from Dhillon *et al.*: [Weighted Graph Cuts without Eigenvectors: A Multilevel Approach](http://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf) (PAMI 2007) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_graclus.py)]
 * **[Voxel Grid Pooling](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.pool.voxel_grid)** from, *e.g.*, Simonovsky and Komodakis: [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://arxiv.org/abs/1704.02901) (CVPR 2017) [[**Example**](https://github.com/pyg-team/pytorch_geometric/blob/master/examples/mnist_voxel_grid.py)]

torch_geometric/nn/dense/mincut_pool.py

@@ -4,8 +4,9 @@
 
 
 def dense_mincut_pool(x, adj, s, mask=None):
-    r"""The MinCUt pooling operator from the `"Mincut Pooling in Graph Neural
-    Networks" <https://arxiv.org/abs/1907.00481>`_ paper
+    r"""The MinCut pooling operator from the `"Spectral Clustering in Graph
+    Neural Networks for Graph Pooling" <https://arxiv.org/abs/1907.00481>`_
+    paper
 
     .. math::
         \mathbf{X}^{\prime} &= {\mathrm{softmax}(\mathbf{S})}^{\top} \cdot
@@ -17,7 +18,7 @@ def dense_mincut_pool(x, adj, s, mask=None):
     based on dense learned assignments :math:`\mathbf{S} \in \mathbb{R}^{B
     \times N \times C}`.
     Returns the pooled node feature matrix, the coarsened and symmetrically
-    normalized adjacency matrix and two auxiliary objectives: (1) The minCUT
+    normalized adjacency matrix and two auxiliary objectives: (1) The MinCut
     loss
 
     .. math::
@@ -67,7 +68,7 @@ def dense_mincut_pool(x, adj, s, mask=None):
     out = torch.matmul(s.transpose(1, 2), x)
     out_adj = torch.matmul(torch.matmul(s.transpose(1, 2), adj), s)
 
-    # MinCUT regularization.
+    # MinCut regularization.
     mincut_num = _rank3_trace(out_adj)
     d_flat = torch.einsum('ijk->ij', adj)
     d = _rank3_diag(d_flat)