Summary of Scalable Implicit Graphon Learning, by Ali Azizpour et al.
Scalable Implicit Graphon Learning
by Ali Azizpour, Nicolas Zilberstein, Santiago Segarra
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Scalable Implicit Graphon Learning (SIGL) method combines implicit neural representations (INRs) and graph neural networks (GNNs) to estimate a graphon from observed graphs. Unlike existing methods, SIGL learns a continuous graphon at arbitrary resolutions, addressing limitations such as fixed resolution and scalability issues. The GNNs determine the correct node ordering, improving graph alignment. Asymptotic consistency of the estimator is characterized, showing that more expressive INRs and GNNs lead to consistent estimators. Evaluation on synthetic and real-world graphs demonstrates SIGL’s superiority over existing methods and its ability to scale effectively to larger graphs, making it suitable for tasks like graph data augmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SIGL is a new way to understand the structure of graphs. Graphs are like networks that connect things together. This method, called Scalable Implicit Graphon Learning (SIGL), uses two different techniques: implicit neural representations (INRs) and graph neural networks (GNNs). These help create a model of the graph’s structure, which is useful for many tasks. SIGL is special because it can make models at any size, not just fixed sizes like before. It also helps align the nodes in the graph better. Scientists tested SIGL on some fake and real graphs and found that it works really well and can handle bigger graphs too. |
Keywords
» Artificial intelligence » Alignment » Data augmentation
