Work

Capturing spatiotemporal long-range dependencies in dynamic graphs

Year: 2024

Type: dissertation

Abstract: In this thesis, models to address the problem of long-range spatiotemporal dependencies in dynamic graphs are presented and tested on both real and synthetic datasets. These models are obtained through a combination of Graph Neural Networks with the NeuralODE paradigm, exploiting theoretical properties of the dynamical system that arise from the ODE describing the model to guarantee stability during training and more efficient propagation of information both in the spatial dimension (between different nodes of the graph) and the temporal dimension (different timestep of the dynamical graph).

Source: Electronic Theses and Dissertations Repository (University of Pisa)

Author: BIONDI, FILIPPO

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7045916360

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