5.4 Graph Classification

Instead of a big single graph, sometimes we might have the data in the form of multiple graphs, for example a list of different types of communities of people. By characterizing the friendships among people in the same community by a graph, we get a list of graphs to classify. In this scenario, a graph classification model could help identify the type of the community, i.e. to classify each graph based on the structure and overall information.


The major difference between graph classification and node classification or link prediction is that the prediction result characterize the property of the entire input graph. We perform the message passing over nodes/edges just like the previous tasks, but also try to retrieve a graph-level representation.

The graph classification proceeds as follows:

Graph Classification Process

Graph Classification Process

From left to right, the common practice is:

  • Prepare graphs in to a batch of graphs

  • Message passing on the batched graphs to update node/edge features

  • Aggregate node/edge features into a graph-level representation

  • Classification head for the task

Batch of Graphs

Usually a graph classification task trains on a lot of graphs, and it will be very inefficient if we use only one graph at a time when training the model. Borrowing the idea of mini-batch training from common deep learning practice, we can build a batch of multiple graphs and send them together for one training iteration.

In DGL, we can build a single batched graph of a list of graphs. This batched graph can be simply used as a single large graph, with separated components representing the corresponding original small graphs.

Batched Graph

Batched Graph

Graph Readout

Every graph in the data may have its unique structure, as well as its node and edge features. In order to make a single prediction, we usually aggregate and summarize over the possibly abundant information. This type of operation is named Readout. Common aggregations include summation, average, maximum or minimum over all node or edge features.

Given a graph \(g\), we can define the average readout aggregation as

\[h_g = \frac{1}{|\mathcal{V}|}\sum_{v\in \mathcal{V}}h_v\]

In DGL the corresponding function call is dgl.readout_nodes().

Once \(h_g\) is available, we can pass it through an MLP layer for classification output.

Writing neural network model

The input to the model is the batched graph with node and edge features. One thing to note is the node and edge features in the batched graph have no batch dimension. A little special care should be put in the model:

Computation on a batched graph

Next, we discuss the computational properties of a batched graph.

First, different graphs in a batch are entirely separated, i.e. no edge connecting two graphs. With this nice property, all message passing functions still have the same results.

Second, the readout function on a batched graph will be conducted over each graph separately. Assume the batch size is \(B\) and the feature to be aggregated has dimension \(D\), the shape of the readout result will be \((B, D)\).

g1 = dgl.graph(([0, 1], [1, 0]))
g1.ndata['h'] = torch.tensor([1., 2.])
g2 = dgl.graph(([0, 1], [1, 2]))
g2.ndata['h'] = torch.tensor([1., 2., 3.])

dgl.readout_nodes(g1, 'h')
# tensor([3.])  # 1 + 2

bg = dgl.batch([g1, g2])
dgl.readout_nodes(bg, 'h')
# tensor([3., 6.])  # [1 + 2, 1 + 2 + 3]

Finally, each node/edge feature tensor on a batched graph is in the format of concatenating the corresponding feature tensor from all graphs.

# tensor([1., 2., 1., 2., 3.])

Model definition

Being aware of the above computation rules, we can define a very simple model.

class Classifier(nn.Module):
    def __init__(self, in_dim, hidden_dim, n_classes):
        super(Classifier, self).__init__()
        self.conv1 = dglnn.GraphConv(in_dim, hidden_dim)
        self.conv2 = dglnn.GraphConv(hidden_dim, hidden_dim)
        self.classify = nn.Linear(hidden_dim, n_classes)

    def forward(self, g, feat):
        # Apply graph convolution and activation.
        h = F.relu(self.conv1(g, h))
        h = F.relu(self.conv2(g, h))
        with g.local_scope():
            g.ndata['h'] = h
            # Calculate graph representation by average readout.
            hg = dgl.mean_nodes(g, 'h')
            return self.classify(hg)

Training loop

Data Loading

Once the model’s defined, we can start training. Since graph classification deals with lots of relative small graphs instead of a big single one, we usually can train efficiently on stochastic mini-batches of graphs, without the need to design sophisticated graph sampling algorithms.

Assuming that we have a graph classification dataset as introduced in Chapter 4: Graph Data Pipeline.

import dgl.data
dataset = dgl.data.GINDataset('MUTAG', False)

Each item in the graph classification dataset is a pair of a graph and its label. We can speed up the data loading process by taking advantage of the DataLoader, by customizing the collate function to batch the graphs:

def collate(samples):
    graphs, labels = map(list, zip(*samples))
    batched_graph = dgl.batch(graphs)
    batched_labels = torch.tensor(labels)
    return batched_graph, batched_labels

Then one can create a DataLoader that iterates over the dataset of graphs in minibatches.

from torch.utils.data import DataLoader
dataloader = DataLoader(


Training loop then simply involves iterating over the dataloader and updating the model.

model = Classifier(10, 20, 5)
opt = torch.optim.Adam(model.parameters())
for epoch in range(20):
    for batched_graph, labels in dataloader:
        feats = batched_graph.ndata['feats']
        logits = model(batched_graph, feats)
        loss = F.cross_entropy(logits, labels)

DGL implements GIN as an example of graph classification. The training loop is inside the function train in main.py. The model implementation is inside gin.py with more components such as using dgl.nn.pytorch.GINConv (also available in MXNet and Tensorflow) as the graph convolution layer, batch normalization, etc.

Heterogeneous graph

Graph classification with heterogeneous graphs is a little different from that with homogeneous graphs. Except that you need heterogeneous graph convolution modules, yoyu also need to aggregate over the nodes of different types in the readout function.

The following shows an example of summing up the average of node representations for each node type.

class RGCN(nn.Module):
    def __init__(self, in_feats, hid_feats, out_feats, rel_names):

        self.conv1 = dglnn.HeteroGraphConv({
            rel: dglnn.GraphConv(in_feats, hid_feats)
            for rel in rel_names}, aggregate='sum')
        self.conv2 = dglnn.HeteroGraphConv({
            rel: dglnn.GraphConv(hid_feats, out_feats)
            for rel in rel_names}, aggregate='sum')

    def forward(self, graph, inputs):
        # inputs are features of nodes
        h = self.conv1(graph, inputs)
        h = {k: F.relu(v) for k, v in h.items()}
        h = self.conv2(graph, h)
        return h

class HeteroClassifier(nn.Module):
    def __init__(self, in_dim, hidden_dim, n_classes, rel_names):

        self.rgcn = RGCN(in_dim, hidden_dim, hidden_dim, rel_names)
        self.classify = nn.Linear(hidden_dim, n_classes)

    def forward(self, g):
        h = g.ndata['feat']
        h = self.rgcn(g, h)
        with g.local_scope():
            g.ndata['h'] = h
            # Calculate graph representation by average readout.
            hg = 0
            for ntype in g.ntypes:
                hg = hg + dgl.mean_nodes(g, 'h', ntype=ntype)
            return self.classify(hg)

The rest of the code is not different from that for homogeneous graphs.

# etypes is the list of edge types as strings.
model = HeteroClassifier(10, 20, 5, etypes)
opt = torch.optim.Adam(model.parameters())
for epoch in range(20):
    for batched_graph, labels in dataloader:
        logits = model(batched_graph)
        loss = F.cross_entropy(logits, labels)