Summary of Generating Graphs Via Spectral Diffusion, by Giorgia Minello et al.
Generating Graphs via Spectral Diffusion
by Giorgia Minello, Alessandro Bicciato, Luca Rossi, Andrea Torsello, Luca Cosmo
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces GGSD, a novel graph generative model that leverages spectral decomposition of the graph Laplacian matrix and a diffusion process. It combines denoising and eigenvector sampling to reconstruct the graph Laplacian and adjacency matrix, allowing it to capture structural characteristics while avoiding complexity bottlenecks. The model is accelerated using spectrum truncation and a transformer-based architecture linear in node count. Experiments on synthetic and real-world graphs demonstrate its performance against state-of-the-art alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GGSD is a new way to create graph structures based on the properties of the graph’s shape. It works by taking the Laplacian, which shows how connected nodes are, and sampling from it to generate new nodes. This process avoids some limitations of other methods that try to do similar things. The model can also handle features attached to each node. In tests with both made-up and real data, GGSD outperforms existing models. |
Keywords
* Artificial intelligence * Diffusion * Generative model * Transformer
