• geometric deep learning
• graph neural network
• k-plex
• pooling
• pseudoclique

This thesis proposes a novel pooling technique for Graph Neural Networks based on k-plexes, i.e., pseudo-cliques where each node can miss up to k links to the other nodes within the same subgraph. This pooling method, named KPlexPool, is completely non-parametrical, and generates a coarsen graph exploiting only its topological information. This model was tested on graph classification tasks using standard benchmark datasets from literature, and its performance was compared with the ones obtained from other parametrical pooling methods, showing state-of-the-art results.