21 citations · 21 across the 6 of their papers we have counts for
7 papers
Latent Flow Matching for Molecular Graph Generation
Mathis Goupillon, Roman Bresson, Konstantinos Divriotis +1
Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In thi…
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
Hugo Schnoering, Roman Bresson, Michalis Vazirgiannis
Bitcoin's pseudonymous nature makes it challenging to analyze user-level activity, since a single user may control multiple identifiers (addresses). Existing heuristic-based method…
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2
We introduce GraViti, a transformer-based graph-level variational autoencoder that encodes entire graphs into single, fixed-dimensional latent vectors rather than per-node embeddin…
Learning to Rank the Initial Branching Order of SAT Solvers
Arvid Eriksson, Gabriel Poesia, Roman Bresson +2
Finding good branching orders is key to solving SAT problems efficiently, but finding such branching orders is a difficult problem. Using a learning based approach to predict a goo…
Prediction via Shapley Value Regression
Amr Alkhatib, Roman Bresson, Henrik Boström +1
Shapley values have several desirable, theoretically well-supported, properties for explaining black-box model predictions. Traditionally, Shapley values are computed post-hoc, lea…
Obtaining Example-Based Explanations from Deep Neural Networks
Genghua Dong, Henrik Boström, Michalis Vazirgiannis +1
Most techniques for explainable machine learning focus on feature attribution, i.e., values are assigned to the features such that their sum equals the prediction. Example attribut…
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Roman Bresson, Giannis Nikolentzos, George Panagopoulos +3
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood a…