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dgl/docs/source/api/python/dgl.rst
Minjie Wang 8a07ab7737 [Doc] Tutorials re-organization (#2683)
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.. _apidgl:
dgl
=============================
.. currentmodule:: dgl
.. automodule:: dgl
.. _api-graph-create-ops:
Graph Create Ops
-------------------------
Operators for constructing :class:`DGLGraph` from raw data formats.
.. autosummary::
:toctree: ../../generated/
graph
heterograph
from_scipy
from_networkx
bipartite_from_scipy
bipartite_from_networkx
rand_graph
rand_bipartite
knn_graph
segmented_knn_graph
create_block
block_to_graph
.. _api-subgraph-extraction:
Subgraph Extraction Ops
-------------------------------------
Operators for extracting and returning subgraphs.
.. autosummary::
:toctree: ../../generated/
node_subgraph
edge_subgraph
node_type_subgraph
edge_type_subgraph
in_subgraph
out_subgraph
.. _api-transform:
Graph Transform Ops
----------------------------------
Operators for generating new graphs by manipulating the structure of the existing ones.
.. autosummary::
:toctree: ../../generated/
add_nodes
add_edges
remove_nodes
remove_edges
add_self_loop
remove_self_loop
add_reverse_edges
reverse
to_bidirected
to_simple
to_block
compact_graphs
to_heterogeneous
to_homogeneous
to_networkx
line_graph
khop_graph
metapath_reachable_graph
.. _api-batch:
Batching and Reading Out Ops
-------------------------------
Operators for batching multiple graphs into one for batch processing and
operators for computing graph-level representation for both single and batched graphs.
.. autosummary::
:toctree: ../../generated/
batch
unbatch
readout_nodes
readout_edges
sum_nodes
sum_edges
mean_nodes
mean_edges
max_nodes
max_edges
softmax_nodes
softmax_edges
broadcast_nodes
broadcast_edges
topk_nodes
topk_edges
Adjacency Related Utilities
-------------------------------
Utilities for computing adjacency matrix and Lapacian matrix.
.. autosummary::
:toctree: ../../generated/
khop_adj
laplacian_lambda_max
Graph Traversal & Message Propagation
------------------------------------------
DGL implements graph traversal algorithms implemented as python generators,
which returns the visited set of nodes or edges (in ID tensor) at each iteration.
The naming convention is ``<algorithm>_[nodes|edges]_generator``.
An example usage is as follows.
.. code:: python
g = ... # some DGLGraph
for nodes in dgl.bfs_nodes_generator(g, 0):
do_something(nodes)
.. autosummary::
:toctree: ../../generated/
bfs_nodes_generator
bfs_edges_generator
topological_nodes_generator
dfs_edges_generator
dfs_labeled_edges_generator
DGL provides APIs to perform message passing following graph traversal order. ``prop_nodes_XXX``
calls traversal algorithm ``XXX`` and triggers :func:`~DGLGraph.pull()` on the visited node
set at each iteration. ``prop_edges_YYY`` applies traversal algorithm ``YYY`` and triggers
:func:`~DGLGraph.send_and_recv()` on the visited edge set at each iteration.
.. autosummary::
:toctree: ../../generated/
prop_nodes
prop_nodes_bfs
prop_nodes_topo
prop_edges
prop_edges_dfs
Utilities
-----------------------------------------------
Other utilities for controlling randomness, saving and loading graphs, etc.
.. autosummary::
:toctree: ../../generated/
seed
save_graphs
load_graphs