Yes, it is possible to use graph neural networks (GNN) for clustering a set of graphs rather than nodes using MATLAB's deep learning libraries. However, it is important to note that GNNs are typically designed for node-level tasks such as node classification and link prediction. Therefore, you may need to adapt the GNN architecture to work with graph-level clustering tasks.
Here's how you can get started:
1. Preprocess and transform your graph data into a format that can be used for training a GNN. This may involve encoding each graph as a matrix or tensor that captures its structure and features.
2. Choose a GNN architecture that is suitable for the clustering task. There are several types of GNN models, such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Auto-Encoders (GAE) that can be used for different types of graph data.
3. Train the GNN using your graph data and labels. You can use MATLAB's built-in deep learning tools to define the GNN model, train it on a set of graph data, and evaluate its performance using standard metrics.
4. Once the GNN is trained, you can use it for clustering new sets of graphs. Simply provide your input graphs as input to the trained GNN model and use its outputs to assign each graph to a cluster.
Keep in mind that GNNs may not always be the best approach for graph clustering tasks, especially if the graphs have a large number of nodes or edges. In such cases, other clustering approaches such as partition-based algorithms or spectral clustering may be more appropriate.
