1 code implementation • 25 Mar 2024 • Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis
Learning graph generative models over latent spaces has received less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance.
no code implementations • 13 Jun 2023 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
Despite advances in generative methods, accurately modeling the distribution of graphs remains a challenging task primarily because of the absence of predefined or inherent unique graph representation.
1 code implementation • 1 Dec 2022 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
no code implementations • 7 Dec 2021 • Yoann Boget, Magda Gregorova, Alexandros Kalousis
One solution consists of using equivariant generative functions, which ensure the ordering invariance.
1 code implementation • 18 Oct 2019 • Yoann Boget
By sampling $z$, we can therefore obtain samples following approximately $p(x|y)$, which is the predictive distribution of $x$ for a new $y$.