320 citations · 512 across the 9 of their papers we have counts for
10 papers
Graph Diffusion that can Insert and Delete
Matteo Ninniri, Marco Podda, Davide Bacciu
Generative models of graphs based on discrete Denoising Diffusion Probabilistic Models (DDPMs) offer a principled approach to molecular generation by systematically removing struct…
A method for the systematic generation of graph XAI benchmarks via Weisfeiler-Leman coloring
Michele Fontanesi, Alessio Micheli, Marco Podda +1
Graph neural networks have become the de facto model for learning from structured data. However, the decision-making process of GNNs remains opaque to the end user, which undermine…
Efficient quantification on large-scale networks
Alessio Micheli, Alejandro Moreo, Marco Podda +3
Network quantification (NQ) is the problem of estimating the proportions of nodes belonging to each class in subsets of unlabelled graph nodes. When prior probability shift is at p…
Towards Efficient Molecular Property Optimization with Graph Energy Based Models
Luca Miglior, Lorenzo Simone, Marco Podda +1
Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chem…
Classifier-free graph diffusion for molecular property targeting
Matteo Ninniri, Marco Podda, Davide Bacciu
This work focuses on the task of property targeting: that is, generating molecules conditioned on target chemical properties to expedite candidate screening for novel drug and mate…
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
Marco Podda, Davide Bacciu
The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several…