2_basics.py

"""
.. currentmodule:: dgl

DGL Basics
==========

**Author**: `Minjie Wang <https://jermainewang.github.io/>`_, Quan Gan, Yu Gai,
Zheng Zhang

The Goal of this tutorial:

* To create a graph.
* To read and write node and edge representations.
"""

###############################################################################
# Graph Creation
# --------------
# The design of :class:`DGLGraph` was influenced by other graph libraries. Indeed,
# you can create a graph from networkx, and convert it into a :class:`DGLGraph` and
# vice versa:

import networkx as nx
import dgl

g_nx = nx.petersen_graph()
g_dgl = dgl.DGLGraph(g_nx)

import matplotlib.pyplot as plt
plt.subplot(121)
nx.draw(g_nx, with_labels=True)
plt.subplot(122)
nx.draw(g_dgl.to_networkx(), with_labels=True)

plt.show()


###############################################################################
# They are the same graph, except that :class:`DGLGraph` is *always* directional.
#
# One can also create a graph by calling DGL's own interface.
# 
# Now let's build a star graph. :class:`DGLGraph` nodes are consecutive range of
# integers between 0 and :func:`number_of_nodes() <DGLGraph.number_of_nodes>`
# and can grow by calling :func:`add_nodes <DGLGraph.add_nodes>`.
# :class:`DGLGraph` edges are in order of their additions. Note that
# edges are accessed in much the same way as nodes, with one extra feature
# of *edge broadcasting*:

import dgl
import torch as th

g = dgl.DGLGraph()
g.add_nodes(10)
# a couple edges one-by-one
for i in range(1, 4):
    g.add_edge(i, 0)
# a few more with a paired list
src = list(range(5, 8)); dst = [0]*3
g.add_edges(src, dst)
# finish with a pair of tensors
src = th.tensor([8, 9]); dst = th.tensor([0, 0])
g.add_edges(src, dst)

# edge broadcasting will do star graph in one go!
g.clear(); g.add_nodes(10)
src = th.tensor(list(range(1, 10)));
g.add_edges(src, 0)

import networkx as nx
import matplotlib.pyplot as plt
nx.draw(g.to_networkx(), with_labels=True)
plt.show()


###############################################################################
# Feature Assignment
# ------------------
# One can also assign features to nodes and edges of a :class:`DGLGraph`.  The
# features are represented as dictionary of names (strings) and tensors,
# called **fields**.
#
# The following code snippet assigns each node a vector (len=3).
#
# .. note::
#
#    DGL aims to be framework-agnostic, and currently it supports PyTorch and
#    MXNet tensors. From now on, we use PyTorch as an example.

import dgl
import torch as th

x = th.randn(10, 3)
g.ndata['x'] = x


###############################################################################
# :func:`ndata <DGLGraph.ndata>` is a syntax sugar to access states of all nodes,
# states are stored
# in a container ``data`` that hosts user defined dictionary.

print(g.ndata['x'] == g.nodes[:].data['x'])

# access node set with integer, list, or integer tensor
g.nodes[0].data['x'] = th.zeros(1, 3)
g.nodes[[0, 1, 2]].data['x'] = th.zeros(3, 3)
g.nodes[th.tensor([0, 1, 2])].data['x'] = th.zeros(3, 3)


###############################################################################
# Assigning edge features is in a similar fashion to that of node features,
# except that one can also do it by specifying endpoints of the edges.

g.edata['w'] = th.randn(9, 2)

# access edge set with IDs in integer, list, or integer tensor
g.edges[1].data['w'] = th.randn(1, 2)
g.edges[[0, 1, 2]].data['w'] = th.zeros(3, 2)
g.edges[th.tensor([0, 1, 2])].data['w'] = th.zeros(3, 2)

# one can also access the edges by giving endpoints
g.edges[1, 0].data['w'] = th.ones(1, 2)                 # edge 1 -> 0
g.edges[[1, 2, 3], [0, 0, 0]].data['w'] = th.ones(3, 2) # edges [1, 2, 3] -> 0


###############################################################################
# After assignments, each node/edge field will be associated with a scheme
# containing the shape and data type (dtype) of its field value.

print(g.node_attr_schemes())
g.ndata['x'] = th.zeros((10, 4))
print(g.node_attr_schemes())


###############################################################################
# One can also remove node/edge states from the graph. This is particularly
# useful to save memory during inference.

g.ndata.pop('x')
g.edata.pop('w')


###############################################################################
# Multigraphs
# ~~~~~~~~~~~
# Many graph applications need multi-edges. To enable this, construct :class:`DGLGraph`
# with ``multigraph=True``.

g_multi = dgl.DGLGraph(multigraph=True)
g_multi.add_nodes(10)
g_multi.ndata['x'] = th.randn(10, 2)

g_multi.add_edges(list(range(1, 10)), 0)
g_multi.add_edge(1, 0) # two edges on 1->0

g_multi.edata['w'] = th.randn(10, 2)
g_multi.edges[1].data['w'] = th.zeros(1, 2)
print(g_multi.edges())


###############################################################################
# An edge in multi-graph cannot be uniquely identified using its incident nodes
# :math:`u` and :math:`v`; query their edge ids use ``edge_id`` interface.

eid_10 = g_multi.edge_id(1, 0)
g_multi.edges[eid_10].data['w'] = th.ones(len(eid_10), 2)
print(g_multi.edata['w'])


###############################################################################
# .. note::
#
#    * Nodes and edges can be added but not removed; we will support removal in
#      the future.
#    * Updating a feature of different schemes raise error on indivdual node (or
#      node subset).


###############################################################################
# Next steps
# ----------
# In the :doc:`next tutorial <3_pagerank>`, we will go through the
# DGL message passing interface by implementing PageRank.