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Unifying Generation and Prediction On Graphs With Latent Graph Diffusion by Debraj Das is a document available to read on EtoBox.

What is Unifying Generation and Prediction On Graphs With Latent Graph Diffusion about?

This paper introduces Latent Graph Diffusion (LGD), a novel framework that unifies various graph learning tasks, including generation, regression, and classification, into a single generative model. LGD leverages a latent space representation to simultaneously generate node, edge, and graph-level features, overcoming challenges posed by discrete graph structures. Experimental results demonstrate that LGD achieves state-of-the-art performance across a range of graph tasks, marking a significant advancement i

Author

Debraj Das

Language

EN