André Panisson

Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given n objects and a pairwise similarity function, the goal is to cluster the objects so that similar objects are put in the same cluster and dissimilar objects are put in different clusters. Despite its versatility, existing CC algorithms suffer from significant scalability issues and are inherently transductive: i.e., the algorithm must be executed from scratch for any new problem instance. In this work, we bridge this gap by leveraging Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances. By learning to exploit common structural patterns and node features during training, our framework generalizes to new graphs drawn from the same distribution with minimal computational overhead with respect to standard algorithms. We demonstrate the effectiveness and scalability of our approach through extensive experiments. Our framework not only excels in the inductive setting, e.g., lowering the inference time up to 5 order of magnitude, while maintaining an approximation ratio within ~10% of the best baseline solution, but also achieves competitive results on standard (transductive) CC benchmarks. Finally, we showcase a practical application of our framework as a learnable pooling mechanism for graph classification. Our results indicate that our method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in networks.

Detailed socioeconomic insights are essential for effective urban policy, yet traditional census data remains static, costly, and restricted to coarse spatial resolutions due to privacy constraints. To bridge this ''granularity gap'', we introduce a Socioeconomic Super-Resolution framework that infers fine-grained indicators from user-generated digital traces. % We propose a Spatial-GNN framework that learns business representations from user-business interaction graphs, explicitly enriching them with semantic categories and coarse geographical context. We show that these embeddings, trained solely on coarse aggregate labels (Postal Codes), capture sufficient latent signal to recover fine-grained attributes at the Census Block Group level. % Experiments on the Yelp dataset confirm that our approach effectively disaggregates urban data, with the learned embeddings naturally capturing spatial and socioeconomic homophily. This work bridges Recommender Systems and Urban Computing, offering a scalable methodology for high-resolution demographic inference.

Proprietary data is a valuable asset used to develop predictive algorithms that benefit a wide range of users, including customers, business owners, and decision-makers. Consequently, there is a growing interest in developing safe and robust techniques for sharing, learning models, and distributing predictions across a wide spectrum of potential stakeholders. However, a structured process to assess the value of data assets, and thus enabling collaborations among stakeholders, remains largely unexplored. This is particularly challenging when the data to be shared has a networked structure, where increasing the shared data samples potentially connects information observed by different data owners, providing new knowledge that is unavailable to any data owner individually. Here, we propose E-GraDE, a framework that assists organizations in assessing the value of their networked data to better address graph machine learning tasks. This framework includes a step-by-step analysis of the requirements of different stakeholders, such as the accuracy or fairness requisites of the models, ensuring a fair evaluation process and stronger alignment in the development of a data federation consortium. Additionally, we propose an approach to estimate the value of networked data to be shared while disclosing only a small fraction of the original information. We support our approach with extensive computational experiments, analysing each part of it through simulated use cases.