📝 Description

The goal is to explore and improve GNN-based approaches to cluster the vertices of a graph. The result of clustering can be used to generate a smaller coarsened graph.

This project focuses on developing and implementing methodologies for clustering the vertices of an attributed graph, i.e., a graph with information associated with its vertices and edges. Based on the clustering results, a smaller graph can be built to summarize as good as possible the information of the original graph. Such an approach can be seen as an equivalent of standard pooling in Convolutional Neural Networks, where the spatial information is gradually coarsened to gradually distill more high-level information from an image. As convolutional layers are interleaved with pooling layers in CNNs, message-passing layers are iterleaved with graph pooling operations in GNNs.

The project will focus on extending general approaches for clustering into a Graph Neural Network framework. The methodologies will be tested on two tasks:

• Perform clustering on the vertices of large graphs;
• Use graph pooling in GNNs for graph-level tasks such as graph classification and regression;
• Implement the methodologies in our library for graph pooling, Torch Geometric Pool.

📚 References

• Pooling in Graph Neural Networks
• Spectral clustering with Graph Neural Networks
• Torch Geometric Pool

📨 Contact

Filippo Maria Bianchi (filippo.m.bianchi@uit.no), Carlo Abate (carlo.abate@uit.no)