Summary of Ifh: a Diffusion Framework For Flexible Design Of Graph Generative Models, by Samuel Cognolato et al.
IFH: a Diffusion Framework for Flexible Design of Graph Generative Models
by Samuel Cognolato, Alessandro Sperduti, Luciano Serafini
First submitted to arxiv on: 23 Aug 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 The proposed Graph Generative Model, called Insert-Fill-Halt (IFH), offers a novel approach to generating graphs by controlling the level of sequentiality. By leveraging Denoising Diffusion Probabilistic Models (DDPM) and designing a node removal process followed by an insertion process, IFH enables users to specify their desired sequentiality degree. The model’s performance is evaluated in terms of quality, run time, and memory, demonstrating its effectiveness across different sequentiality levels. Furthermore, the authors show that incorporating DiGress, a diffusion-based one-shot model, as a generative step within IFH leads to improved results and competitive performance with state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs can be generated in two main ways: one-shot or sequential. A new approach called Insert-Fill-Halt (IFH) lets you choose how much like a sequence the graph should look. It works by taking a graph, removing some nodes and edges, then adding them back in to make it more sequential. The authors tested this method and found that it works well and is fast. They also showed that combining IFH with another model called DiGress makes it even better. |
Keywords
» Artificial intelligence » Diffusion » Generative model » One shot
