Summary of L2g2g: a Scalable Local-to-global Network Embedding with Graph Autoencoders, by Ruikang Ouyang et al.
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
by Ruikang Ouyang, Andrew Elliott, Stratis Limnios, Mihai Cucuringu, Gesine Reinert
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI); 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 method, L2G2G, improves graph representation learning by dynamically synchronizing latent node representations during training. This approach achieves higher accuracy than standard Local2Global methods while maintaining scalability. The model leverages more information from the graph by aligning local embeddings in each epoch, making it suitable for large and dense networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary L2G2G is a new way to learn about real-world networks. It helps make predictions about nodes and edges by creating a good low-dimensional representation of the network. This method is fast and gets good results. The key idea is to adjust the node representations as you train, making it work better on large networks. |
Keywords
* Artificial intelligence * Representation learning
