SOURCE-LINKED INTELLIGENCE

Unifying Neural Relational Inference

CORDIS · observation · Publication date unknown

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 machin

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recordType

award

status

SIGNED

region

EU

value

251578.56

unit

EUR