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Unverified Commit 3bd5a9b6 authored by Mufei Li's avatar Mufei Li Committed by GitHub
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[Doc] Fix Doc (#3777) 

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docs/source/api/python/index.rst
View file @ 3bd5a9b6
......@@ -10,7 +10,9 @@ API Reference
dgl.DGLGraph
dgl.distributed
dgl.function
nn
nn-pytorch
nn-tensorflow
nn-mxnet
dgl.ops
dgl.sampling
dgl.contrib.UnifiedTensor
......
docs/source/api/python/nn-mxnet.rst 0 → 100644
View file @ 3bd5a9b6
.. _apinn-mxnet:
dgl.nn (MXNet)
================
Conv Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.mxnet.conv.GraphConv
~dgl.nn.mxnet.conv.RelGraphConv
~dgl.nn.mxnet.conv.TAGConv
~dgl.nn.mxnet.conv.GATConv
~dgl.nn.mxnet.conv.EdgeConv
~dgl.nn.mxnet.conv.SAGEConv
~dgl.nn.mxnet.conv.SGConv
~dgl.nn.mxnet.conv.APPNPConv
~dgl.nn.mxnet.conv.GINConv
~dgl.nn.mxnet.conv.GatedGraphConv
~dgl.nn.mxnet.conv.GMMConv
~dgl.nn.mxnet.conv.ChebConv
~dgl.nn.mxnet.conv.AGNNConv
~dgl.nn.mxnet.conv.NNConv
Dense Conv Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.mxnet.conv.DenseGraphConv
~dgl.nn.mxnet.conv.DenseSAGEConv
~dgl.nn.mxnet.conv.DenseChebConv
Global Pooling Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.mxnet.glob.SumPooling
~dgl.nn.mxnet.glob.AvgPooling
~dgl.nn.mxnet.glob.MaxPooling
~dgl.nn.mxnet.glob.SortPooling
~dgl.nn.mxnet.glob.GlobalAttentionPooling
~dgl.nn.mxnet.glob.Set2Set
Heterogeneous Learning Modules
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.mxnet.HeteroGraphConv
Utility Modules
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.mxnet.utils.Sequential
docs/source/api/python/nn-pytorch.rst 0 → 100644
View file @ 3bd5a9b6
.. _apinn-pytorch:
dgl.nn (PyTorch)
================
Conv Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.conv.GraphConv
~dgl.nn.pytorch.conv.EdgeWeightNorm
~dgl.nn.pytorch.conv.RelGraphConv
~dgl.nn.pytorch.conv.TAGConv
~dgl.nn.pytorch.conv.GATConv
~dgl.nn.pytorch.conv.GATv2Conv
~dgl.nn.pytorch.conv.EGATConv
~dgl.nn.pytorch.conv.EdgeConv
~dgl.nn.pytorch.conv.SAGEConv
~dgl.nn.pytorch.conv.SGConv
~dgl.nn.pytorch.conv.APPNPConv
~dgl.nn.pytorch.conv.GINConv
~dgl.nn.pytorch.conv.GatedGraphConv
~dgl.nn.pytorch.conv.GMMConv
~dgl.nn.pytorch.conv.ChebConv
~dgl.nn.pytorch.conv.AGNNConv
~dgl.nn.pytorch.conv.NNConv
~dgl.nn.pytorch.conv.AtomicConv
~dgl.nn.pytorch.conv.CFConv
~dgl.nn.pytorch.conv.DotGatConv
~dgl.nn.pytorch.conv.TWIRLSConv
~dgl.nn.pytorch.conv.TWIRLSUnfoldingAndAttention
~dgl.nn.pytorch.conv.GCN2Conv
Dense Conv Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.conv.DenseGraphConv
~dgl.nn.pytorch.conv.DenseSAGEConv
~dgl.nn.pytorch.conv.DenseChebConv
Global Pooling Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.glob.SumPooling
~dgl.nn.pytorch.glob.AvgPooling
~dgl.nn.pytorch.glob.MaxPooling
~dgl.nn.pytorch.glob.SortPooling
~dgl.nn.pytorch.glob.WeightAndSum
~dgl.nn.pytorch.glob.GlobalAttentionPooling
~dgl.nn.pytorch.glob.Set2Set
~dgl.nn.pytorch.glob.SetTransformerEncoder
~dgl.nn.pytorch.glob.SetTransformerDecoder
Score Modules for Link Prediction and Knowledge Graph Completion
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.link.EdgePredictor
~dgl.nn.pytorch.link.TransE
~dgl.nn.pytorch.link.TransR
Heterogeneous Learning Modules
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.HeteroGraphConv
~dgl.nn.pytorch.HeteroLinear
~dgl.nn.pytorch.HeteroEmbedding
~dgl.nn.pytorch.TypedLinear
Utility Modules
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.pytorch.utils.Sequential
~dgl.nn.pytorch.utils.WeightBasis
~dgl.nn.pytorch.factory.KNNGraph
~dgl.nn.pytorch.factory.SegmentedKNNGraph
~dgl.nn.pytorch.utils.JumpingKnowledge
~dgl.nn.pytorch.sparse_emb.NodeEmbedding
~dgl.nn.pytorch.explain.GNNExplainer
docs/source/api/python/nn-tensorflow.rst 0 → 100644
View file @ 3bd5a9b6
.. _apinn-tensorflow:
dgl.nn (TensorFlow)
================
Conv Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.tensorflow.conv.GraphConv
~dgl.nn.tensorflow.conv.RelGraphConv
~dgl.nn.tensorflow.conv.GATConv
~dgl.nn.tensorflow.conv.SAGEConv
~dgl.nn.tensorflow.conv.ChebConv
~dgl.nn.tensorflow.conv.SGConv
~dgl.nn.tensorflow.conv.APPNPConv
~dgl.nn.tensorflow.conv.GINConv
Global Pooling Layers
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.tensorflow.glob.SumPooling
~dgl.nn.tensorflow.glob.AvgPooling
~dgl.nn.tensorflow.glob.MaxPooling
~dgl.nn.tensorflow.glob.SortPooling
~dgl.nn.tensorflow.glob.GlobalAttentionPooling
Heterogeneous Learning Modules
----------------------------------------
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~dgl.nn.tensorflow.glob.HeteroGraphConv
docs/source/api/python/nn.rst deleted 100644 → 0
View file @ 6d9433b0
.. _apinn:
dgl.nn
==========
PyTorch
----------------------------------------
Conv Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.pytorch.conv
.. automodule:: dgl.nn.pytorch.conv
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
GraphConv
EdgeWeightNorm
RelGraphConv
TAGConv
GATConv
GATv2Conv
EGATConv
EdgeConv
SAGEConv
SGConv
APPNPConv
GINConv
GatedGraphConv
GMMConv
ChebConv
AGNNConv
NNConv
AtomicConv
CFConv
DotGatConv
TWIRLSConv
TWIRLSUnfoldingAndAttention
GCN2Conv
Dense Conv Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
DenseGraphConv
DenseSAGEConv
DenseChebConv
Global Pooling Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.pytorch.glob
.. automodule:: dgl.nn.pytorch.glob
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
SumPooling
AvgPooling
MaxPooling
SortPooling
WeightAndSum
GlobalAttentionPooling
Set2Set
SetTransformerEncoder
SetTransformerDecoder
Score Modules for Link Prediction and Knowledge Graph Completion
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.pytorch.link
.. automodule:: dgl.nn.pytorch.link
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
EdgePredictor
TransE
TransR
Heterogeneous Learning Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.pytorch
.. automodule:: dgl.nn.pytorch
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
HeteroGraphConv
HeteroLinear
HeteroEmbedding
TypedLinear
Utility Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~utils.Sequential
~utils.WeightBasis
~factory.KNNGraph
~factory.SegmentedKNNGraph
~utils.JumpingKnowledge
~sparse_emb.NodeEmbedding
~explain.GNNExplainer
TensorFlow
----------------------------------------
Conv Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.tensorflow.conv
.. automodule:: dgl.nn.tensorflow.conv
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
GraphConv
RelGraphConv
GATConv
SAGEConv
ChebConv
SGConv
APPNPConv
GINConv
Global Pooling Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.tensorflow.glob
.. automodule:: dgl.nn.tensorflow.glob
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
SumPooling
AvgPooling
MaxPooling
SortPooling
GlobalAttentionPooling
Heterogeneous Learning Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.tensorflow
.. automodule:: dgl.nn.tensorflow
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
HeteroGraphConv
MXNet
----------------------------------------
Conv Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.mxnet.conv
.. automodule:: dgl.nn.mxnet.conv
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
GraphConv
RelGraphConv
TAGConv
GATConv
EdgeConv
SAGEConv
SGConv
APPNPConv
GINConv
GatedGraphConv
GMMConv
ChebConv
AGNNConv
NNConv
Dense Conv Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
DenseGraphConv
DenseSAGEConv
DenseChebConv
Global Pooling Layers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.mxnet.glob
.. automodule:: dgl.nn.mxnet.glob
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
SumPooling
AvgPooling
MaxPooling
SortPooling
GlobalAttentionPooling
Set2Set
Heterogeneous Learning Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. currentmodule:: dgl.nn.mxnet
.. automodule:: dgl.nn.mxnet
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
HeteroGraphConv
Utility Modules
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: ../../generated/
:nosignatures:
:template: classtemplate.rst
~utils.Sequential
docs/source/index.rst
View file @ 3bd5a9b6
......@@ -44,7 +44,9 @@ Welcome to Deep Graph Library Tutorials and Documentation
api/python/dgl.distributed
api/python/dgl.function
api/python/dgl.geometry
api/python/nn
api/python/nn-pytorch
api/python/nn-tensorflow
api/python/nn-mxnet
api/python/nn.functional
api/python/dgl.ops
api/python/dgl.optim
......
python/dgl/transforms/module.py
View file @ 3bd5a9b6
......@@ -50,11 +50,7 @@ __all__ = [
]
def update_graph_structure(g, data_dict, copy_edata=True):
r"""
Description
-----------
Update the structure of a graph.
r"""Update the structure of a graph.
Parameters
----------
......@@ -93,12 +89,7 @@ def update_graph_structure(g, data_dict, copy_edata=True):
return new_g
class BaseTransform:
r"""
Description
-----------
An abstract class for writing transforms.
"""
r"""An abstract class for writing transforms."""
def __call__(self, g):
raise NotImplementedError
......@@ -106,11 +97,7 @@ class BaseTransform:
return self.__class__.__name__ + '()'
class AddSelfLoop(BaseTransform):
r"""
Description
-----------
Add self-loops for each node in the graph and return a new graph.
r"""Add self-loops for each node in the graph and return a new graph.
For heterogeneous graphs, self-loops are added only for edge types with same
source and destination node types.
......@@ -210,11 +197,7 @@ class AddSelfLoop(BaseTransform):
return g
class RemoveSelfLoop(BaseTransform):
r"""
Description
-----------
Remove self-loops for each node in the graph and return a new graph.
r"""Remove self-loops for each node in the graph and return a new graph.
For heterogeneous graphs, this operation only applies to edge types with same
source and destination node types.
......@@ -246,11 +229,7 @@ class RemoveSelfLoop(BaseTransform):
(tensor([1]), tensor([2]))
"""
def transform_etype(self, c_etype, g):
r"""
Description
-----------
Transform the graph corresponding to a canonical edge type.
r"""Transform the graph corresponding to a canonical edge type.
Parameters
----------
......@@ -275,11 +254,7 @@ class RemoveSelfLoop(BaseTransform):
return g
class AddReverse(BaseTransform):
r"""
Description
-----------
Add a reverse edge :math:`(i,j)` for each edge :math:`(j,i)` in the input graph and
r"""Add a reverse edge :math:`(i,j)` for each edge :math:`(j,i)` in the input graph and
return a new graph.
For a heterogeneous graph, it adds a "reverse" edge type for each edge type
......@@ -343,11 +318,7 @@ class AddReverse(BaseTransform):
self.sym_new_etype = sym_new_etype
def transform_symmetric_etype(self, c_etype, g, data_dict):
r"""
Description
-----------
Transform the graph corresponding to a symmetric canonical edge type.
r"""Transform the graph corresponding to a symmetric canonical edge type.
Parameters
----------
......@@ -366,11 +337,7 @@ class AddReverse(BaseTransform):
data_dict[c_etype] = (src, dst)
def transform_asymmetric_etype(self, c_etype, g, data_dict):
r"""
Description
-----------
Transform the graph corresponding to an asymmetric canonical edge type.
r"""Transform the graph corresponding to an asymmetric canonical edge type.
Parameters
----------
......@@ -389,11 +356,7 @@ class AddReverse(BaseTransform):
})
def transform_etype(self, c_etype, g, data_dict):
r"""
Description
-----------
Transform the graph corresponding to a canonical edge type.
r"""Transform the graph corresponding to a canonical edge type.
Parameters
----------
......@@ -434,11 +397,7 @@ class AddReverse(BaseTransform):
return new_g
class ToSimple(BaseTransform):
r"""
Description
-----------
Convert a graph to a simple graph without parallel edges and return a new graph.
r"""Convert a graph to a simple graph without parallel edges and return a new graph.
Parameters
----------
......@@ -496,11 +455,7 @@ class ToSimple(BaseTransform):
aggregator=self.aggregator)
class LineGraph(BaseTransform):
r"""
Description
-----------
Return the line graph of the input graph.
r"""Return the line graph of the input graph.
The line graph :math:`L(G)` of a given graph :math:`G` is a graph where
the nodes in :math:`L(G)` correspond to the edges in :math:`G`. For a pair
......@@ -553,11 +508,7 @@ class LineGraph(BaseTransform):
return functional.line_graph(g, backtracking=self.backtracking, shared=True)
class KHopGraph(BaseTransform):
r"""
Description
-----------
Return the graph whose edges connect the :math:`k`-hop neighbors of the original graph.
r"""Return the graph whose edges connect the :math:`k`-hop neighbors of the original graph.
This module only works for homogeneous graphs.
......@@ -585,13 +536,10 @@ class KHopGraph(BaseTransform):
return functional.khop_graph(g, self.k)
class AddMetaPaths(BaseTransform):
r"""
r"""Add new edges to an input graph based on given metapaths, as described in
`Heterogeneous Graph Attention Network <https://arxiv.org/abs/1903.07293>`__.
Description
-----------
Add new edges to an input graph based on given metapaths, as described in
`Heterogeneous Graph Attention Network <https://arxiv.org/abs/1903.07293>`__. Formally,
a metapath is a path of the form
Formally, a metapath is a path of the form
.. math::
......@@ -655,11 +603,7 @@ class AddMetaPaths(BaseTransform):
return new_g
class Compose(BaseTransform):
r"""
Description
-----------
Create a transform composed of multiple transforms in sequence.
r"""Create a transform composed of multiple transforms in sequence.
Parameters
----------
......@@ -692,12 +636,8 @@ class Compose(BaseTransform):
return self.__class__.__name__ + '([\n' + ',\n'.join(args) + '\n])'
class GCNNorm(BaseTransform):
r"""
Description
-----------
Apply symmetric adjacency normalization to an input graph and save the result edge weights,
as described in `Semi-Supervised Classification with Graph Convolutional Networks
r"""Apply symmetric adjacency normalization to an input graph and save the result edge
weights, as described in `Semi-Supervised Classification with Graph Convolutional Networks
<https://arxiv.org/abs/1609.02907>`__.
For a heterogeneous graph, this only applies to symmetric canonical edge types, whose source
......@@ -768,15 +708,12 @@ class GCNNorm(BaseTransform):
return g
class PPR(BaseTransform):
r"""
Description
-----------
Apply personalized PageRank (PPR) to an input graph for diffusion, as introduced in
r"""Apply personalized PageRank (PPR) to an input graph for diffusion, as introduced in
`The pagerank citation ranking: Bringing order to the web
<http://ilpubs.stanford.edu:8090/422/>`__. A sparsification will be applied to the
weighted adjacency matrix after diffusion. Specifically, edges whose weight is below
a threshold will be dropped.
<http://ilpubs.stanford.edu:8090/422/>`__.
A sparsification will be applied to the weighted adjacency matrix after diffusion.
Specifically, edges whose weight is below a threshold will be dropped.
This module only works for homogeneous graphs.
......@@ -819,11 +756,7 @@ class PPR(BaseTransform):
self.avg_degree = avg_degree
def get_eps(self, num_nodes, mat):
r"""
Description
-----------
Get the threshold for graph sparsification.
r"""Get the threshold for graph sparsification.
"""
if self.eps is None:
# Infer from self.avg_degree
......@@ -884,13 +817,10 @@ def is_bidirected(g):
# pylint: disable=C0103
class HeatKernel(BaseTransform):
r"""
Description
-----------
Apply heat kernel to an input graph for diffusion, as introduced in
r"""Apply heat kernel to an input graph for diffusion, as introduced in
`Diffusion kernels on graphs and other discrete structures
<https://www.ml.cmu.edu/research/dap-papers/kondor-diffusion-kernels.pdf>`__.
A sparsification will be applied to the weighted adjacency matrix after diffusion.
Specifically, edges whose weight is below a threshold will be dropped.
......@@ -935,11 +865,7 @@ class HeatKernel(BaseTransform):
self.avg_degree = avg_degree
def get_eps(self, num_nodes, mat):
r"""
Description
-----------
Get the threshold for graph sparsification.
r"""Get the threshold for graph sparsification.
"""
if self.eps is None:
# Infer from self.avg_degree
......@@ -983,14 +909,11 @@ class HeatKernel(BaseTransform):
return new_g
class GDC(BaseTransform):
r"""
r"""Apply graph diffusion convolution (GDC) to an input graph, as introduced in
`Diffusion Improves Graph Learning <https://www.in.tum.de/daml/gdc/>`__.
Description
-----------
Apply graph diffusion convolution (GDC) to an input graph, as introduced in
`Diffusion Improves Graph Learning <https://www.in.tum.de/daml/gdc/>`__. A sparsification
will be applied to the weighted adjacency matrix after diffusion. Specifically, edges whose
weight is below a threshold will be dropped.
A sparsification will be applied to the weighted adjacency matrix after diffusion.
Specifically, edges whose weight is below a threshold will be dropped.
This module only works for homogeneous graphs.
......@@ -1033,12 +956,7 @@ class GDC(BaseTransform):
self.avg_degree = avg_degree
def get_eps(self, num_nodes, mat):
r"""
Description
-----------
Get the threshold for graph sparsification.
"""
r"""Get the threshold for graph sparsification."""
if self.eps is None:
# Infer from self.avg_degree
if self.avg_degree > num_nodes:
......@@ -1079,11 +997,7 @@ class GDC(BaseTransform):
return new_g
class NodeShuffle(BaseTransform):
r"""
Description
-----------
Randomly shuffle the nodes.
r"""Randomly shuffle the nodes.
Example
-------
......@@ -1116,11 +1030,7 @@ class NodeShuffle(BaseTransform):
# pylint: disable=C0103
class DropNode(BaseTransform):
r"""
Description
-----------
Randomly drop nodes, as described in
r"""Randomly drop nodes, as described in
`Graph Contrastive Learning with Augmentations <https://arxiv.org/abs/2010.13902>`__.
Parameters
......@@ -1166,11 +1076,7 @@ class DropNode(BaseTransform):
# pylint: disable=C0103
class DropEdge(BaseTransform):
r"""
Description
-----------
Randomly drop edges, as described in
r"""Randomly drop edges, as described in
`DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
<https://arxiv.org/abs/1907.10903>`__ and `Graph Contrastive Learning with Augmentations
<https://arxiv.org/abs/2010.13902>`__.
......@@ -1214,11 +1120,7 @@ class DropEdge(BaseTransform):
return g
class AddEdge(BaseTransform):
r"""
Description
-----------
Randomly add edges, as described in `Graph Contrastive Learning with Augmentations
r"""Randomly add edges, as described in `Graph Contrastive Learning with Augmentations
<https://arxiv.org/abs/2010.13902>`__.
Parameters
......
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