Graph classification focuses on assigning labels or categories to entire graphs or networks. Unlike traditional classification tasks that deal with individual data instances, graph classification considers the entire graph structure, including nodes, edges and their properties. A graph classifier uses a mapping function that can accurately predict the class or label of an unseen graph based on its structural properties. The mapping function is learned during training using supervised learning.

#### Why Do We Need Graph Classification?

The importance of graph classification lies in graph data being ubiquitous in today's interconnected world. Graph based methods including graph classification have emerged as methodology of choice in numerous applications across various domains, including:

1. Bioinformatics: Classifying protein-protein interaction networks or gene regulatory networks can provide insights into disease mechanisms and aid in drug discovery. In fact, the most well-known success story of graph neural networks is the discovery of antibiotics to treat drug-resistant diseases, widely reported in early 2020.

3. Cybersecurity: Classifying computer networks can assist in detecting malicious activities, identifying vulnerabilities, and preventing cyber attacks.

4. Chemistry: Classifying molecular graphs can aid in predicting chemical properties, synthesizing new compounds, and understanding chemical reactions.

#### How Do We Build a Graph Classifier?

There are two main approaches that we can use to build graph classifiers: kernel-based methods and neural network-based methods.1. Kernel-based Methods:

These methods rely on defining similarity measures (kernels) between pairs of graphs, which capture their structural and topological properties. Popular kernel-based methods include the random walk kernel, the shortest-path kernel, and the Weisfeiler-Lehman kernel. Once the kernel is defined, traditional kernel-based machine learning algorithms, such as Support Vector Machines (SVMs), can be applied for classification.

2. Neural Network- based Methods:

These methods typically involve learning low-dimensional representations (embeddings) of the graphs through specialized neural network architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). The learned embeddings capture the structural information of the graphs and can be used as input to standard classifiers, like feed-forward neural networks or logistic regression models. For details on GCNs and node embeddings, please visit my earlier post on graph convolutional networks.

Both kernel-based and neural network-based methods have their strengths and weaknesses, and the choice depends on factors such as the size and complexity of the graphs, the availability of labeled data, and computational resources. Given that graph neural networks are getting more mileage, we will complete this blog post by going over steps needed for building a GNN classifier.

#### Steps for Building a GNN Classifier

#### Dataset

We are going to use the MUTAG dataset which is part of the TUDatasets, an extensive collection of graph datasets and easily accessible in PyTorch Geometric library for building graph applications. The MUTAG dataset is a small dataset of 188 graphs representing two classes of graphs. Each graph node is characterized by seven features. Two of the example graphs from this dataset are shown below.

Two example graphs from MUTAG dataset |

We will use 150 graphs for training and the remaining 38 for testing. The division into the training and test sets is done using the available utilities in the PyTorch Geometric.

#### Mini-Batching

Due to the smaller graph sizes in the dataset, mini-batching of graphs is a desirable step in graph classification for better utilization of GPU resources. The mini-batching is done by diagonally stacking adjacency matrices of the graphs in a batch to create a giant graph that acts as an input to the GNN for learning. The node features of the graphs are concatenated to form the corresponding giant node feature vector. The idea of mini-batching is illustrated below.

Illustration of mini-batching |

#### Graph Classifier

*readout layer*. The function of this stage is to aggregate node embeddings into a single vector. We will simply take the average of node embeddings to create readout vectors. PyTorch Geometric has a built-in function for this purpose operating at the mini-batch level that we will use. The final stage is the actual classifier that looks at mapped/readout vectors to learn classification rule. In the present case, we will simply use a linear thresholding classifier to perform binary classification. We need to specify a suitable loss function so that the network can be trained to learn the proper weights.