# DGLGraph and Node/edge Features¶

Author: Minjie Wang, Quan Gan, Yu Gai, Zheng Zhang

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()


There are many ways to construct a DGLGraph. Below are the allowed data types ordered by our recommendataion.

• A pair of arrays (u, v) storing the source and destination nodes respectively. They can be numpy arrays or tensor objects from the backend framework.
• scipy sparse matrix representing the adjacency matrix of the graph to be constructed.
• networkx graph object.
• A list of edges in the form of integer pairs.

The examples below construct the same star graph via different methods.

DGLGraph nodes are a consecutive range of integers between 0 and number_of_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 torch as th
import numpy as np
import scipy.sparse as spp

# Create a star graph from a pair of arrays (using numpy.array works too).
u = th.tensor([0, 0, 0, 0, 0])
v = th.tensor([1, 2, 3, 4, 5])
star1 = dgl.DGLGraph((u, v))

# Create the same graph in one go! Essentially, if one of the arrays is a scalar,
# the value is automatically broadcasted to match the length of the other array
# -- a feature called *edge broadcasting*.
start2 = dgl.DGLGraph((0, v))

# Create the same graph from a scipy sparse matrix (using scipy.sparse.csr_matrix works too).


You can also create a graph by progressively adding more nodes and edges. Although it is not as efficient as the above constructors, it is suitable for applications where the graph cannot be constructed in one shot.

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

# Edge broadcasting will do star graph in one go!
src = th.tensor(list(range(1, 10)));

# Visualize the graph.
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).

Note

DGL aims to be framework-agnostic, and currently it supports PyTorch and MXNet tensors. The following examples use PyTorch only.

import dgl
import torch as th

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


ndata is a syntax sugar to access the feature data of all nodes. To get the features of some particular nodes, slice out the corresponding rows.

g.ndata['x'][0] = th.zeros(1, 3)
g.ndata['x'][[0, 1, 2]] = th.zeros(3, 3)
g.ndata['x'][th.tensor([0, 1, 2])] = th.randn((3, 3))


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.edata['w'][1] = th.randn(1, 2)
g.edata['w'][[0, 1, 2]] = th.zeros(3, 2)
g.edata['w'][th.tensor([0, 1, 2])] = th.zeros(3, 2)

# You can get the edge ids by giving endpoints, which are useful for accessing the features.
g.edata['w'][g.edge_id(1, 0)] = th.ones(1, 2)                   # edge 1 -> 0
g.edata['w'][g.edge_ids([1, 2, 3], [0, 0, 0])] = th.ones(3, 2)  # edges [1, 2, 3] -> 0
# Use edge broadcasting whenever applicable.
g.edata['w'][g.edge_ids([1, 2, 3], 0)] = 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())


Out:

{'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')


### Working with multigraphs¶

Many graph applications need parallel edges, which class:DGLGraph supports by default.

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

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())


Out:

(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, return_array=True)
g_multi.edges[eid_10].data['w'] = th.ones(len(eid_10), 2)
print(g_multi.edata['w'])


Out:

tensor([[ 1.0000,  1.0000],
[ 0.0000,  0.0000],
[-0.3486, -0.7761],
[ 1.1867, -0.2986],
[ 1.3186,  0.4512],
[-0.6735,  1.1000],
[-0.7579, -0.5126],
[ 0.6677,  1.6272],
[-1.3767, -0.3174],
[ 1.0000,  1.0000]])


Note

• Nodes and edges can be added but not removed.
• Updating a feature of different schemes raises the risk of error on individual nodes (or node subset).

## Next steps¶

In the next tutorial you learn the DGL message passing interface by implementing PageRank.

Total running time of the script: ( 0 minutes 0.524 seconds)

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