wehub-resource-sync
118 lines
4.1 KiB
ReStructuredText
118 lines
4.1 KiB
ReStructuredText
.. _guide-graph-external:
|
|
|
|
1.4 Creating Graphs from External Sources
|
|
-----------------------------------------
|
|
|
|
:ref:`(中文版)<guide_cn-graph-external>`
|
|
|
|
The options to construct a :class:`~dgl.DGLGraph` from external sources include:
|
|
|
|
- Conversion from external python libraries for graphs and sparse matrices (NetworkX and SciPy).
|
|
- Loading graphs from disk.
|
|
|
|
The section does not cover functions that generate graphs by transforming from other
|
|
graphs. See the API reference manual for an overview of them.
|
|
|
|
Creating Graphs from External Libraries
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
The following code snippet is an example for creating a graph from a SciPy sparse matrix and a NetworkX graph.
|
|
|
|
.. code::
|
|
|
|
>>> import dgl
|
|
>>> import torch as th
|
|
>>> import scipy.sparse as sp
|
|
>>> spmat = sp.rand(100, 100, density=0.05) # 5% nonzero entries
|
|
>>> dgl.from_scipy(spmat) # from SciPy
|
|
Graph(num_nodes=100, num_edges=500,
|
|
ndata_schemes={}
|
|
edata_schemes={})
|
|
|
|
>>> import networkx as nx
|
|
>>> nx_g = nx.path_graph(5) # a chain 0-1-2-3-4
|
|
>>> dgl.from_networkx(nx_g) # from networkx
|
|
Graph(num_nodes=5, num_edges=8,
|
|
ndata_schemes={}
|
|
edata_schemes={})
|
|
|
|
Note that when constructing from the `nx.path_graph(5)`, the resulting :class:`~dgl.DGLGraph` has 8
|
|
edges instead of 4. This is because `nx.path_graph(5)` constructs an undirected NetworkX graph
|
|
:class:`networkx.Graph` while a :class:`~dgl.DGLGraph` is always directed. In converting an undirected
|
|
NetworkX graph into a :class:`~dgl.DGLGraph`, DGL internally converts undirected edges to two directed edges.
|
|
Using directed NetworkX graphs :class:`networkx.DiGraph` can avoid such behavior.
|
|
|
|
.. code::
|
|
|
|
>>> nxg = nx.DiGraph([(2, 1), (1, 2), (2, 3), (0, 0)])
|
|
>>> dgl.from_networkx(nxg)
|
|
Graph(num_nodes=4, num_edges=4,
|
|
ndata_schemes={}
|
|
edata_schemes={})
|
|
|
|
.. note::
|
|
|
|
DGL internally converts SciPy matrices and NetworkX graphs to tensors to construct graphs.
|
|
Hence, these construction methods are not meant for performance critical parts.
|
|
|
|
See APIs: :func:`dgl.from_scipy`, :func:`dgl.from_networkx`.
|
|
|
|
Loading Graphs from Disk
|
|
^^^^^^^^^^^^^^^^^^^^^^^^
|
|
|
|
There are many data formats for storing graphs and it isn't possible to enumerate every option.
|
|
Thus, this section only gives some general pointers on certain common ones.
|
|
|
|
Comma Separated Values (CSV)
|
|
""""""""""""""""""""""""""""
|
|
|
|
One very common format is CSV, which stores nodes, edges, and their features in a tabular format:
|
|
|
|
.. table:: nodes.csv
|
|
|
|
+-----------+
|
|
|age, title |
|
|
+===========+
|
|
|43, 1 |
|
|
+-----------+
|
|
|23, 3 |
|
|
+-----------+
|
|
|... |
|
|
+-----------+
|
|
|
|
.. table:: edges.csv
|
|
|
|
+-----------------+
|
|
|src, dst, weight |
|
|
+=================+
|
|
|0, 1, 0.4 |
|
|
+-----------------+
|
|
|0, 3, 0.9 |
|
|
+-----------------+
|
|
|... |
|
|
+-----------------+
|
|
|
|
There are known Python libraries (e.g. pandas) for loading this type of data into python
|
|
objects (e.g., :class:`numpy.ndarray`), which can then be used to construct a DGLGraph. If the
|
|
backend framework also provides utilities to save/load tensors from disk (e.g., :func:`torch.save`,
|
|
:func:`torch.load`), one can follow the same principle to build a graph.
|
|
|
|
See also: `Tutorial for loading a Karate Club Network from edge pairs CSV <https://github.com/dglai/WWW20-Hands-on-Tutorial/blob/master/basic_tasks/1_load_data.ipynb>`_.
|
|
|
|
JSON/GML Format
|
|
"""""""""""""""
|
|
|
|
Though not particularly fast, NetworkX provides many utilities to parse
|
|
`a variety of data formats <https://networkx.github.io/documentation/stable/reference/readwrite/index.html>`_
|
|
which indirectly allows DGL to create graphs from these sources.
|
|
|
|
DGL Binary Format
|
|
"""""""""""""""""
|
|
|
|
DGL provides APIs to save and load graphs from disk stored in binary format. Apart from the
|
|
graph structure, the APIs also handle feature data and graph-level label data. DGL also
|
|
supports checkpointing graphs directly to S3 or HDFS. The reference manual provides more
|
|
details about the usage.
|
|
|
|
See APIs: :func:`dgl.save_graphs`, :func:`dgl.load_graphs`.
|