# Batched Graph Classification with DGL¶

Author: Mufei Li, Minjie Wang, Zheng Zhang.

Graph classification is an important problem with applications across many fields – bioinformatics, chemoinformatics, social network analysis, urban computing and cyber-security. Applying graph neural networks to this problem has been a popular approach recently ( Ying et al., 2018, Cangea et al., 2018, Knyazev et al., 2018, Bianchi et al., 2019, Liao et al., 2019, Gao et al., 2019).

This tutorial demonstrates:
• batching multiple graphs of variable size and shape with DGL
• training a graph neural network for a simple graph classification task

In this tutorial, we will learn how to perform batched graph classification with dgl via a toy example of classifying 8 types of regular graphs as below:

We implement a synthetic dataset data.MiniGCDataset in DGL. The dataset has 8 different types of graphs and each class has the same number of graph samples.

from dgl.data import MiniGCDataset
import matplotlib.pyplot as plt
import networkx as nx
# A dataset with 80 samples, each graph is
# of size [10, 20]
dataset = MiniGCDataset(80, 10, 20)
graph, label = dataset[0]
fig, ax = plt.subplots()
nx.draw(graph.to_networkx(), ax=ax)
ax.set_title('Class: {:d}'.format(label))
plt.show()


## Form a graph mini-batch¶

To train neural networks more efficiently, a common practice is to batch multiple samples together to form a mini-batch. Batching fixed-shaped tensor inputs is quite easy (for example, batching two images of size $$28\times 28$$ gives a tensor of shape $$2\times 28\times 28$$). By contrast, batching graph inputs has two challenges:

• Graphs are sparse.
• Graphs can have various length (e.g. number of nodes and edges).

To address this, DGL provides a dgl.batch() API. It leverages the trick that a batch of graphs can be viewed as a large graph that have many disjoint connected components. Below is a visualization that gives the general idea:

We define the following collate function to form a mini-batch from a given list of graph and label pairs.

import dgl

def collate(samples):
# The input samples is a list of pairs
#  (graph, label).
graphs, labels = map(list, zip(*samples))
batched_graph = dgl.batch(graphs)
return batched_graph, torch.tensor(labels)


The return type of dgl.batch() is still a graph (similar to the fact that a batch of tensors is still a tensor). This means that any code that works for one graph immediately works for a batch of graphs. More importantly, since DGL processes messages on all nodes and edges in parallel, this greatly improves efficiency.

## Graph Classifier¶

The graph classification can be proceeded as follows:

From a batch of graphs, we first perform message passing/graph convolution for nodes to “communicate” with others. After message passing, we compute a tensor for graph representation from node (and edge) attributes. This step may be called “readout/aggregation” interchangeably. Finally, the graph representations can be fed into a classifier $$g$$ to predict the graph labels.

## Graph Convolution¶

Our graph convolution operation is basically the same as that for GCN (checkout our tutorial). The only difference is that we replace $$h_{v}^{(l+1)} = \text{ReLU}\left(b^{(l)}+\sum_{u\in\mathcal{N}(v)}h_{u}^{(l)}W^{(l)}\right)$$ by $$h_{v}^{(l+1)} = \text{ReLU}\left(b^{(l)}+\frac{1}{|\mathcal{N}(v)|}\sum_{u\in\mathcal{N}(v)}h_{u}^{(l)}W^{(l)}\right)$$. The replacement of summation by average is to balance nodes with different degrees, which gives a better performance for this experiment.

Note that the self edges added in the dataset initialization allows us to include the original node feature $$h_{v}^{(l)}$$ when taking the average.

import dgl.function as fn
import torch
import torch.nn as nn

# Sends a message of node feature h.
msg = fn.copy_src(src='h', out='m')

def reduce(nodes):
"""Take an average over all neighbor node features hu and use it to
overwrite the original node feature."""
accum = torch.mean(nodes.mailbox['m'], 1)
return {'h': accum}

class NodeApplyModule(nn.Module):
"""Update the node feature hv with ReLU(Whv+b)."""
def __init__(self, in_feats, out_feats, activation):
super(NodeApplyModule, self).__init__()
self.linear = nn.Linear(in_feats, out_feats)
self.activation = activation

def forward(self, node):
h = self.linear(node.data['h'])
h = self.activation(h)
return {'h' : h}

class GCN(nn.Module):
def __init__(self, in_feats, out_feats, activation):
super(GCN, self).__init__()
self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)

def forward(self, g, feature):
# Initialize the node features with h.
g.ndata['h'] = feature
g.update_all(msg, reduce)
g.apply_nodes(func=self.apply_mod)
return g.ndata.pop('h')


For this demonstration, we consider initial node features to be their degrees. After two rounds of graph convolution, we perform a graph readout by averaging over all node features for each graph in the batch

$h_g=\frac{1}{|\mathcal{V}|}\sum_{v\in\mathcal{V}}h_{v}$

In DGL, dgl.mean_nodes() handles this task for a batch of graphs with variable size. We then feed our graph representations into a classifier with one linear layer to obtain pre-softmax logits.

import torch.nn.functional as F

class Classifier(nn.Module):
def __init__(self, in_dim, hidden_dim, n_classes):
super(Classifier, self).__init__()

self.layers = nn.ModuleList([
GCN(in_dim, hidden_dim, F.relu),
GCN(hidden_dim, hidden_dim, F.relu)])
self.classify = nn.Linear(hidden_dim, n_classes)

def forward(self, g):
# For undirected graphs, in_degree is the same as
# out_degree.
h = g.in_degrees().view(-1, 1).float()
for conv in self.layers:
h = conv(g, h)
g.ndata['h'] = h
hg = dgl.mean_nodes(g, 'h')
return self.classify(hg)


## Setup and Training¶

We create a synthetic dataset of $$400$$ graphs with $$10$$ ~ $$20$$ nodes. $$320$$ graphs constitute a training set and $$80$$ graphs constitute a test set.

import torch.optim as optim

# Create training and test sets.
trainset = MiniGCDataset(320, 10, 20)
testset = MiniGCDataset(80, 10, 20)
# Use PyTorch's DataLoader and the collate function
# defined before.
collate_fn=collate)

# Create model
model = Classifier(1, 256, trainset.num_classes)
loss_func = nn.CrossEntropyLoss()
model.train()

epoch_losses = []
for epoch in range(80):
epoch_loss = 0
for iter, (bg, label) in enumerate(data_loader):
prediction = model(bg)
loss = loss_func(prediction, label)
loss.backward()
optimizer.step()
epoch_loss += loss.detach().item()
epoch_loss /= (iter + 1)
print('Epoch {}, loss {:.4f}'.format(epoch, epoch_loss))
epoch_losses.append(epoch_loss)


Out:

Epoch 0, loss 2.1314
Epoch 1, loss 1.9733
Epoch 2, loss 1.8137
Epoch 3, loss 1.7241
Epoch 4, loss 1.6616
Epoch 5, loss 1.5648
Epoch 6, loss 1.4834
Epoch 7, loss 1.4058
Epoch 8, loss 1.3181
Epoch 9, loss 1.2749
Epoch 10, loss 1.2216
Epoch 11, loss 1.1611
Epoch 12, loss 1.1049
Epoch 13, loss 1.0653
Epoch 14, loss 1.0426
Epoch 15, loss 1.0075
Epoch 16, loss 0.9786
Epoch 17, loss 0.9725
Epoch 18, loss 0.9512
Epoch 19, loss 0.9408
Epoch 20, loss 0.8893
Epoch 21, loss 0.8558
Epoch 22, loss 0.8338
Epoch 23, loss 0.8528
Epoch 24, loss 0.8457
Epoch 25, loss 0.8466
Epoch 26, loss 0.8132
Epoch 27, loss 0.7790
Epoch 28, loss 0.7891
Epoch 29, loss 0.7683
Epoch 30, loss 0.7563
Epoch 31, loss 0.7424
Epoch 32, loss 0.7168
Epoch 33, loss 0.7108
Epoch 34, loss 0.7036
Epoch 35, loss 0.6904
Epoch 36, loss 0.6884
Epoch 37, loss 0.6928
Epoch 38, loss 0.6934
Epoch 39, loss 0.6907
Epoch 40, loss 0.6568
Epoch 41, loss 0.6502
Epoch 42, loss 0.6218
Epoch 43, loss 0.6194
Epoch 44, loss 0.6129
Epoch 45, loss 0.6070
Epoch 46, loss 0.6041
Epoch 47, loss 0.5972
Epoch 48, loss 0.6029
Epoch 49, loss 0.5908
Epoch 50, loss 0.5874
Epoch 51, loss 0.5518
Epoch 52, loss 0.5547
Epoch 53, loss 0.5381
Epoch 54, loss 0.5424
Epoch 55, loss 0.5475
Epoch 56, loss 0.5234
Epoch 57, loss 0.5326
Epoch 58, loss 0.5352
Epoch 59, loss 0.5161
Epoch 60, loss 0.5139
Epoch 61, loss 0.4968
Epoch 62, loss 0.4959
Epoch 63, loss 0.5089
Epoch 64, loss 0.5143
Epoch 65, loss 0.5406
Epoch 66, loss 0.4845
Epoch 67, loss 0.4814
Epoch 68, loss 0.4980
Epoch 69, loss 0.4963
Epoch 70, loss 0.4876
Epoch 71, loss 0.4656
Epoch 72, loss 0.4605
Epoch 73, loss 0.4566
Epoch 74, loss 0.4404
Epoch 75, loss 0.4682
Epoch 76, loss 0.4563
Epoch 77, loss 0.4361
Epoch 78, loss 0.4435
Epoch 79, loss 0.4470


The learning curve of a run is presented below:

plt.title('cross entropy averaged over minibatches')
plt.plot(epoch_losses)
plt.show()


The trained model is evaluated on the test set created. Note that for deployment of the tutorial, we restrict our running time and you are likely to get a higher accuracy ($$80$$ % ~ $$90$$ %) than the ones printed below.

model.eval()
# Convert a list of tuples to two lists
test_X, test_Y = map(list, zip(*testset))
test_bg = dgl.batch(test_X)
test_Y = torch.tensor(test_Y).float().view(-1, 1)
probs_Y = torch.softmax(model(test_bg), 1)
sampled_Y = torch.multinomial(probs_Y, 1)
argmax_Y = torch.max(probs_Y, 1)[1].view(-1, 1)
print('Accuracy of sampled predictions on the test set: {:.4f}%'.format(
(test_Y == sampled_Y.float()).sum().item() / len(test_Y) * 100))
print('Accuracy of argmax predictions on the test set: {:4f}%'.format(
(test_Y == argmax_Y.float()).sum().item() / len(test_Y) * 100))


Out:

Accuracy of sampled predictions on the test set: 71.2500%
Accuracy of argmax predictions on the test set: 83.750000%


Below is an animation where we plot graphs with the probability a trained model assigns its ground truth label to it:

To understand the node/graph representations a trained model learnt, we use t-SNE, for dimensionality reduction and visualization.

The two small figures on the top separately visualize node representations after $$1$$, $$2$$ layers of graph convolution and the figure on the bottom visualizes the pre-softmax logits for graphs as graph representations.

While the visualization does suggest some clustering effects of the node features, it is expected not to be a perfect result as node degrees are deterministic for our node features. Meanwhile, the graph features are way better separated.

## What’s Next?¶

Graph classification with graph neural networks is still a very young field waiting for folks to bring more exciting discoveries! It is not easy as it requires mapping different graphs to different embeddings while preserving their structural similarity in the embedding space. To learn more about it, “How Powerful Are Graph Neural Networks?” in ICLR 2019 might be a good starting point.

With regards to more examples on batched graph processing, see

Total running time of the script: ( 0 minutes 27.567 seconds)

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