Unifying Neural Relational Inference

Información del proyecto

UNRI

Identificador del acuerdo de subvención: 101268722

DOI

10.3030/101268722

Fecha de la firma de la CE 22 Abril 2026

Fecha de inicio 1 Mayo 2027

Fecha de finalización 30 Abril 2029

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 251 578,56

Coordinado por

UNIVERSITETET I TROMSOE - NORGES ARKTISKE UNIVERSITET
Norway

Objetivo

"Our world, from the human brain to the global economy, is built on complex networks. Understanding the structure of these networks is a cornerstone of modern science and technology, yet these connections are often difficult to spot.
Typically, we only observe a system's dynamic activity over time, such as the fluctuating signals from brain regions or the daily prices of stocks. This gives rise to a critical scientific question: can we create an accurate map of a system's hidden and evolving connections (its latent dynamic relationships) simply by watching the activity of its components? This computational challenge boils down to understanding the underlying rules and relationships in a system, represented by a graph, just by observing how its parts evolve over time. In the field of machine learning, this is known as Neural Relational Inference (NRI). Existing approaches are designed for either static or dynamic graphs, and are almost exclusively transductive, which in the NRI context means being optimized on a single dataset. To address this gap, my project, UNRI, will pioneer a novel class of temporally consistent diffusion models. The core idea is to learn the ""rules of change"" by modeling the generative process of the graph's evolution itself. My model will treat the structure at each timestep as a direct, stochastic evolution from the previous state, ensuring temporal consistency by design. My project will deliver three main contributions. The first is a concrete, high-performance model that solves a pressing technical challenge by operating across all key NRI settings.
Inspired by my supervisor's successful work on unifying a particular field of graph machine learning, as a second contribution, I will develop a general framework for the entire NRI field and use it as the foundation to build a theoretically-grounded open-source library."

Ámbito científico (EuroSciVoc)

• ciencias naturales › informática y ciencias de la información › inteligencia artificial › aprendizaje automático

Palabras clave

• Diffusion models
• Graph Neural Networks
• Generative models on graphs

Programa(s)

• HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) PROGRAMA PRINCIPAL
  

Tema(s)

• HORIZON-MSCA-2025-PF-01-01 - MSCA Postdoctoral Fellowships 2025
  

Régimen de financiación

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

Convocatoria de propuestas

HORIZON-MSCA-2025-PF

Coordinador

UNIVERSITETET I TROMSOE - NORGES ARKTISKE UNIVERSITET

• Aportación neta de la UEn: € 251 578,56
• Dirección: HANSINE HANSENS VEG 14 ; 9019 Tromso ; Noruega
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: Sin datos