DGL Basics

In this tutorial, you learn how to create a graph and how to read and write node and edge representations.

Creating a graph

The design of DGLGraph was influenced by other graph libraries. You can create a graph from networkx and convert it into a 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()

The examples here show the same graph, except that DGLGraph is always directional.

You can also create a graph by calling the DGL interface.

In the next example, you build a star graph. DGLGraph nodes are a consecutive range of integers between 0 and number_of_nodes() and can grow by calling add_nodes. DGLGraph edges are in order of their additions. Note that edges are accessed in much the same way as nodes, with one extra feature: 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()

Assigning a feature

You can also assign features to nodes and edges of a 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).

import dgl
import torch as th

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

ndata is a syntax sugar to access the state of all nodes. States are stored in a container data that hosts a 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)

# Results
tensor([[True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True],
        [True, True, True]])

Assigning edge features is similar to that of node features, except that you 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)

# You 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 or 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())

# Results
{'x': Scheme(shape=(3,), dtype=torch.float32)}
{'x': Scheme(shape=(4,), dtype=torch.float32)}

You can also remove node or edge states from the graph. This is particularly useful to save memory during inference.

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

# Results
tensor([[ 1.0000,  1.0000],
        [ 1.0000,  1.0000],
        [ 1.0000,  1.0000],
        [-0.0935, -1.6118],
        [-0.1696,  1.6565],
        [ 1.4897, -0.9181],
        [ 0.3871, -2.1339],
        [ 1.0246,  1.3848],
        [ 0.3366, -0.4830]])

Working with multigraphs

Many graph applications need parallel edges. To enable this, construct 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())

# Results
(tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 1]), tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))

An edge in multigraph cannot be uniquely identified by using its incident nodes u and 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'])

# Results
tensor([[ 1.0000,  1.0000],
        [ 0.0000,  0.0000],
        [ 0.3942, -0.8382],
        [ 1.9161,  0.0693],
        [ 0.5701, -0.0722],
        [ 0.6398,  0.2236],
        [ 1.4696,  0.0155],
        [-0.3290, -0.9607],
        [ 0.8175, -0.3145],
        [ 1.0000,  1.0000]])

原文地址:
https://docs.dgl.ai/tutorials/basics/2_basics.html