Summary of Glad: Improving Latent Graph Generative Modeling with Simple Quantization, by Van Khoa Nguyen et al.
GLAD: Improving Latent Graph Generative Modeling with Simple Quantization
by Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel graph generative model called GLAD, which operates in a discrete latent space to preserve the inherent structure of graphs. Unlike previous models that assume continuity in the latent space, GLAD uses diffusion bridges to learn the prior distribution of its discrete latent space. By avoiding unnatural assumptions and operating over an appropriately constructed latent space, GLAD achieves competitive performance with state-of-the-art baselines on graph benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLAD is a new way to make computer graphics that looks like real networks. It works by using a special kind of math called diffusion bridges to understand what makes graphs look like they do. This helps it create new graphs that are similar but not the same as the ones we already have. The paper shows that GLAD does this better than other methods and can make good graphs. |
Keywords
» Artificial intelligence » Diffusion » Generative model » Latent space
