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cs.LG2024
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
Ruikang Ouyang, Andrew Elliott, Stratis Limnios +2
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representati…
cs.LG2023
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
Stratis Limnios, Praveen Selvaraj, Mihai Cucuringu +3
Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating…