[Transform] [Doc] Rename transform to transforms and update doc (#3765)

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python/dgl/nn/pytorch/conv/densegraphconv.py

@@ -6,12 +6,9 @@ from torch.nn import init
 
 
 class DenseGraphConv(nn.Module):
-    """
+    """Graph Convolutional layer from `Semi-Supervised Classification with Graph
+    Convolutional Networks <https://arxiv.org/abs/1609.02907>`__
 
-    Description
-    -----------
-    Graph Convolutional Network layer where the graph structure
-    is given by an adjacency matrix.
     We recommend user to use this module when applying graph convolution on
     dense graphs.
 
@@ -92,11 +89,7 @@ class DenseGraphConv(nn.Module):
             init.zeros_(self.bias)
 
     def forward(self, adj, feat):
-        r"""
-
-        Description
-        -----------
-        Compute (Dense) Graph Convolution layer.
+        r"""Compute (Dense) Graph Convolution layer.
 
         Parameters
         ----------

python/dgl/nn/pytorch/conv/densesageconv.py

@@ -5,12 +5,9 @@ from ....utils import check_eq_shape
 
 
 class DenseSAGEConv(nn.Module):
-    """
+    """GraphSAGE layer from `Inductive Representation Learning on Large Graphs
+    <https://arxiv.org/abs/1706.02216>`__
 
-    Description
-    -----------
-    GraphSAGE layer where the graph structure is given by an
-    adjacency matrix.
     We recommend to use this module when appying GraphSAGE on dense graphs.
 
     Note that we only support gcn aggregator in DenseSAGEConv.

python/dgl/nn/pytorch/conv/dotgatconv.py

@@ -9,11 +9,8 @@ from ....utils import expand_as_pair
 
 
 class DotGatConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Apply dot product version of self attention in GCN.
+    r"""Apply dot product version of self attention in `Graph Attention Network
+    <https://arxiv.org/pdf/1710.10903.pdf>`__
 
         .. math::
             h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i, j} h_j^{(l)}

python/dgl/nn/pytorch/conv/edgeconv.py

@@ -8,14 +8,10 @@ from ....utils import expand_as_pair
 
 
 class EdgeConv(nn.Module):
-    r"""
+    r"""EdgeConv layer from `Dynamic Graph CNN for Learning on Point Clouds
+    <https://arxiv.org/pdf/1801.07829>`__
 
-    Description
-    -----------
-    EdgeConv layer.
-
-    Introduced in "`Dynamic Graph CNN for Learning on Point Clouds
-    <https://arxiv.org/pdf/1801.07829>`__".  Can be described as follows:
+    It can be described as follows:
 
     .. math::
        h_i^{(l+1)} = \max_{j \in \mathcal{N}(i)} (

python/dgl/nn/pytorch/conv/egatconv.py

@@ -10,15 +10,10 @@ from ....base import DGLError
 
 # pylint: enable=W0235
 class EGATConv(nn.Module):
-    r"""
-    Description
-    -----------
-    Apply Graph Attention Layer over input graph. EGAT is an extension
-    of regular `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`__
-    handling edge features, detailed description is available in `Rossmann-Toolbox
-    <https://pubmed.ncbi.nlm.nih.gov/34571541/>`__ (see supplementary data).
-    The difference appears in the method how unnormalized attention scores :math:`e_{ij}`
-    are obtained:
+    r"""Graph attention layer that handles edge features from `Rossmann-Toolbox
+    <https://pubmed.ncbi.nlm.nih.gov/34571541/>`__ (see supplementary data)
+
+    The difference lies in how unnormalized attention scores :math:`e_{ij}` are obtained:
 
     .. math::
         e_{ij} &= \vec{F} (f_{ij}^{\prime})
@@ -27,7 +22,7 @@ class EGATConv(nn.Module):
 
     where :math:`f_{ij}^{\prime}` are edge features, :math:`\mathrm{A}` is weight matrix and
 
-    :math: `\vec{F}` is weight vector. After that resulting node features
+    :math: `\vec{F}` is weight vector. After that, resulting node features
     :math:`h_{i}^{\prime}` are updated in the same way as in regular GAT.
 
     Parameters

python/dgl/nn/pytorch/conv/gatconv.py

@@ -11,12 +11,8 @@ from ....utils import expand_as_pair
 
 # pylint: enable=W0235
 class GATConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Apply `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`__
-    over an input signal.
+    r"""Graph attention layer from `Graph Attention Network
+    <https://arxiv.org/pdf/1710.10903.pdf>`__
 
     .. math::
         h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{i,j} W^{(l)} h_j^{(l)}

python/dgl/nn/pytorch/conv/gatedgraphconv.py

@@ -8,12 +8,8 @@ from .... import function as fn
 
 
 class GatedGraphConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Gated Graph Convolution layer from paper `Gated Graph Sequence
-    Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__.
+    r"""Gated Graph Convolution layer from `Gated Graph Sequence
+    Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__
 
     .. math::
         h_{i}^{0} &= [ x_i \| \mathbf{0} ]

python/dgl/nn/pytorch/conv/gatv2conv.py

@@ -11,13 +11,8 @@ from ....utils import expand_as_pair
 
 # pylint: enable=W0235
 class GATv2Conv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Apply GATv2 from
-    `How Attentive are Graph Attention Networks? <https://arxiv.org/pdf/2105.14491.pdf>`__
-    over an input signal.
+    r"""GATv2 from `How Attentive are Graph Attention Networks?
+    <https://arxiv.org/pdf/2105.14491.pdf>`__
 
     .. math::
         h_i^{(l+1)} = \sum_{j\in \mathcal{N}(i)} \alpha_{ij}^{(l)} W^{(l)}_{right} h_j^{(l)}

python/dgl/nn/pytorch/conv/gcn2conv.py

@@ -12,14 +12,10 @@ from .graphconv import EdgeWeightNorm
 
 
 class GCN2Conv(nn.Module):
+    r"""Graph Convolutional Network via Initial residual
+    and Identity mapping (GCNII) from `Simple and Deep Graph Convolutional
+    Networks <https://arxiv.org/abs/2007.02133>`__
 
-    r"""
-
-    Description
-    -----------
-    The Graph Convolutional Network via Initial residual
-    and Identity mapping (GCNII) was introduced in `"Simple and Deep Graph Convolutional
-    Networks" <https://arxiv.org/abs/2007.02133>`_ paper.
     It is mathematically is defined as follows:
 
     .. math::

python/dgl/nn/pytorch/conv/ginconv.py

@@ -8,12 +8,8 @@ from ....utils import expand_as_pair
 
 
 class GINConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Graph Isomorphism Network layer from paper `How Powerful are Graph
-    Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__.
+    r"""Graph Isomorphism Network layer from `How Powerful are Graph
+    Neural Networks? <https://arxiv.org/pdf/1810.00826.pdf>`__
 
     .. math::
         h_i^{(l+1)} = f_\Theta \left((1 + \epsilon) h_i^{l} +

python/dgl/nn/pytorch/conv/gmmconv.py

@@ -11,13 +11,9 @@ from ....utils import expand_as_pair
 
 
 class GMMConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    The Gaussian Mixture Model Convolution layer from `Geometric Deep
+    r"""Gaussian Mixture Model Convolution layer from `Geometric Deep
     Learning on Graphs and Manifolds using Mixture Model CNNs
-    <http://openaccess.thecvf.com/content_cvpr_2017/papers/Monti_Geometric_Deep_Learning_CVPR_2017_paper.pdf>`__.
+    <https://arxiv.org/abs/1611.08402>`__
 
     .. math::
         u_{ij} &= f(x_i, x_j), x_j \in \mathcal{N}(i)

python/dgl/nn/pytorch/conv/graphconv.py

@@ -7,16 +7,12 @@ from torch.nn import init
 from .... import function as fn
 from ....base import DGLError
 from ....utils import expand_as_pair
-from ....transform import reverse
+from ....transforms import reverse
 from ....convert import block_to_graph
 from ....heterograph import DGLBlock
 
 class EdgeWeightNorm(nn.Module):
-    r"""
-
-    Description
-    -----------
-    This module normalizes positive scalar edge weights on a graph
+    r"""This module normalizes positive scalar edge weights on a graph
     following the form in `GCN <https://arxiv.org/abs/1609.02907>`__.
 
     Mathematically, setting ``norm='both'`` yields the following normalization term:
@@ -139,12 +135,10 @@ class EdgeWeightNorm(nn.Module):
 
 # pylint: disable=W0235
 class GraphConv(nn.Module):
-    r"""
+    r"""Graph convolutional layer from `Semi-Supervised Classification with Graph Convolutional
+    Networks <https://arxiv.org/abs/1609.02907>`__
 
-    Description
-    -----------
-    Graph convolution was introduced in `GCN <https://arxiv.org/abs/1609.02907>`__
-    and mathematically is defined as follows:
+    Mathematically it is defined as follows:
 
     .. math::
       h_i^{(l+1)} = \sigma(b^{(l)} + \sum_{j\in\mathcal{N}(i)}\frac{1}{c_{ji}}h_j^{(l)}W^{(l)})

python/dgl/nn/pytorch/conv/hgtconv.py

@@ -9,9 +9,9 @@ from ..linear import TypedLinear
 from ..softmax import edge_softmax
 
 class HGTConv(nn.Module):
-    r"""Heterogeneous graph transformer convolution.
+    r"""Heterogeneous graph transformer convolution from `Heterogeneous Graph Transformer
+    <https://arxiv.org/abs/2003.01332>`__
 
-    Introduced in "`Heterogeneous Graph Transformer <https://arxiv.org/abs/2003.01332>`__".
     Given a graph :math:`G(V, E)` and input node features :math:`H^{(l-1)}`,
     it computes the new node features as follows:
 

python/dgl/nn/pytorch/conv/nnconv.py

@@ -10,12 +10,8 @@ from ....utils import expand_as_pair
 
 
 class NNConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Graph Convolution layer introduced in `Neural Message Passing
-    for Quantum Chemistry <https://arxiv.org/pdf/1704.01212.pdf>`__.
+    r"""Graph Convolution layer from `Neural Message Passing
+    for Quantum Chemistry <https://arxiv.org/pdf/1704.01212.pdf>`__
 
     .. math::
         h_{i}^{l+1} = h_{i}^{l} + \mathrm{aggregate}\left(\left\{

python/dgl/nn/pytorch/conv/relgraphconv.py

@@ -7,11 +7,10 @@ from .... import function as fn
 from ..linear import TypedLinear
 
 class RelGraphConv(nn.Module):
-    r"""Relational graph convolution layer.
+    r"""Relational graph convolution layer from `Modeling Relational Data with Graph
+    Convolutional Networks <https://arxiv.org/abs/1703.06103>`__
 
-    Relational graph convolution is introduced in "`Modeling Relational Data with Graph
-    Convolutional Networks <https://arxiv.org/abs/1703.06103>`__"
-    and can be described in DGL as below:
+    It can be described in as below:
 
     .. math::
 

python/dgl/nn/pytorch/conv/sageconv.py

@@ -10,12 +10,8 @@ from ....utils import expand_as_pair, check_eq_shape, dgl_warning
 
 
 class SAGEConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    GraphSAGE layer from paper `Inductive Representation Learning on
-    Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__.
+    r"""GraphSAGE layer from `Inductive Representation Learning on
+    Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__
 
     .. math::
         h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate}

python/dgl/nn/pytorch/conv/sgconv.py

@@ -9,12 +9,8 @@ from .graphconv import EdgeWeightNorm
 
 
 class SGConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Simplifying Graph Convolution layer from paper `Simplifying Graph
-    Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`__.
+    r"""SGC layer from `Simplifying Graph
+    Convolutional Networks <https://arxiv.org/pdf/1902.07153.pdf>`__
 
     .. math::
         H^{K} = (\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2})^K X \Theta

python/dgl/nn/pytorch/conv/tagconv.py

@@ -8,12 +8,8 @@ from .graphconv import EdgeWeightNorm
 
 
 class TAGConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Topology Adaptive Graph Convolutional layer from paper `Topology
-    Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`__.
+    r"""Topology Adaptive Graph Convolutional layer from `Topology
+    Adaptive Graph Convolutional Networks <https://arxiv.org/pdf/1710.10370.pdf>`__
 
     .. math::
         H^{K} = {\sum}_{k=0}^K (D^{-1/2} A D^{-1/2})^{k} X {\Theta}_{k},

python/dgl/nn/pytorch/conv/twirlsconv.py

@@ -7,12 +7,9 @@ import torch.nn.functional as F
 from .... import function as fn
 
 class TWIRLSConv(nn.Module):
-    r"""
-
-    Description
-    -----------
-    Together with iteratively reweighting least squre from paper `Graph Neural Networks Inspired
-    by Classical Iterative Algorithms <https://arxiv.org/pdf/2103.06064.pdf>`__.
+    r"""Convolution together with iteratively reweighting least squre from
+    `Graph Neural Networks Inspired by Classical Iterative Algorithms
+    <https://arxiv.org/pdf/2103.06064.pdf>`__
 
     Parameters
     ----------

python/dgl/nn/pytorch/explain/gnnexplainer.py

@@ -10,13 +10,10 @@ from ....base import NID, EID
 from ....subgraph import khop_in_subgraph
 
 class GNNExplainer(nn.Module):
-    r"""
+    r"""GNNExplainer model from `GNNExplainer: Generating Explanations for
+    Graph Neural Networks <https://arxiv.org/abs/1903.03894>`__
 
-    Description
-    -----------
-    GNNExplainer model from paper `GNNExplainer: Generating Explanations for
-    Graph Neural Networks <https://arxiv.org/abs/1903.03894>`__ for identifying
-    compact subgraph structures and small subsets of node features that play a
+    It identifies compact subgraph structures and small subsets of node features that play a
     critical role in GNN-based node classification and graph classification.
 
     Parameters