Summary of Towards Characterizing the Value Of Edge Embeddings in Graph Neural Networks, by Dhruv Rohatgi et al.
Towards characterizing the value of edge embeddings in Graph Neural Networks
by Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski
First submitted to arxiv on: 13 Oct 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 explores the benefits of graph neural network (GNN) architectures that maintain and update edge embeddings, a concept inspired by time-space tradeoffs in theoretical computer science. It theoretically shows that under certain conditions, these architectures can be shallower for specific graphical models, and empirically demonstrates that they often improve upon node-based counterparts, particularly in topologies with hub nodes. The authors leverage results from theoretical computer science to develop techniques that optimize edge embedding maintenance and update. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper investigates how graph neural networks (GNNs) can be improved by maintaining and updating edge embeddings. The researchers show that using these architectures can lead to shallower models for certain types of data, and they test their approach on real-world examples. They found that in many cases, this new method outperforms traditional approaches. |
Keywords
» Artificial intelligence » Embedding » Gnn » Graph neural network
