Claudio Meggio

• Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. I Bin, Riccardo De; Salucci, Clara Bertinelli & Hubin, Aliaksandr (Red.), Proceedings of the 40th International Workshop on Statistical Modelling 28.06 – 03.07.2026, Oslo, Norway. International Workshop on Statistical Modelling. ISSN 9788255325000. Fulltekst i vitenarkiv Vis sammendrag

  We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.

• Meggio, Claudio; Pensar, Johan; Badia, David Balcells & Bin, Riccardo De (2026). A path-based boosting algorithm for exploring transition metal compounds. Statistical Modelling. ISSN 1471-082X. doi: 10.1177/1471082x251407458. Fulltekst i vitenarkiv Vis sammendrag

  Motivated by the analysis of chemical metal compounds and their properties for catalysis, we developed a gradient boosting model that explores graph structures to perform prediction tasks. Taking advantage of the iterative nature of boosting, our novel approach, called PathBoost , explores the graphs to identify the relevant paths and simultaneously fits a prediction model. Advantages of PathBoost include automatic variable selection, as only relevant paths are kept in the model and explainability, as a measure of variable importance is provided. The novel algorithm is applied to the tmQM dataset, where the molecules are represented as graphs with atoms as nodes and bonds as edges. The goal is to predict specific quantum properties of the molecules, such as the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) gap. These properties usually require heavy computational power to be computed, while our model aims to provide comparable results using much fewer resources.

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• Cibinel, Luca; Meggio, Claudio & Zuccato, Lorenzo (2026). Enhancing Quantum Graph Representations via Heuristics and Model-Based Clustering. Fulltekst i vitenarkiv Vis sammendrag

  We propose a novel class of representations for graph-structured data, with the objective of improving feature prediction performance. We focus on graphs with clearly identifiable and interpretable subgraphs, such as transition metal complexes. Our approach is based on the identification of subgraph popu- lations whose citizens exhibit a similar statistical behaviour. Knowledge of these populations is injected in the original graphs in the form of virtual nodes to make global information available at the local level.

• Meggio, Claudio; Balcells, David; Pensar, Johan & Bin, Riccardo De (2023). Path Boosting, a statistical learning method for chemistry applications. Fulltekst i vitenarkiv
• Meggio, Claudio; Balcells, David; Bin, Riccardo De & Pensar, Johan (2023). Adapting Gradient Booting methods to graph inputs. Fulltekst i vitenarkiv

Se alle arbeider i NVA