Summary of A Comparative Study on Dynamic Graph Embedding Based on Mamba and Transformers, by Ashish Parmanand Pandey et al.
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
by Ashish Parmanand Pandey, Alan John Varghese, Sarang Patil, Mengjia Xu
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents a comparative study of dynamic graph embedding approaches using transformers and the Mamba architecture. The authors introduce three novel models: TransformerG2G, DG-Mamba, and GDG-Mamba, which leverage graph convolutional networks, state-space models, and graph isomorphism network edge convolutions. Experimental results on multiple benchmark datasets demonstrate that Mamba-based models achieve comparable or superior performance to transformer-based approaches in link prediction tasks while offering significant computational efficiency gains. The study highlights the ability of DG-Mamba variants to consistently outperform transformer-based models on datasets with high temporal variability, such as UCI, Bitcoin, and Reality Mining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different ways to represent changing networks using computer algorithms. It shows that a new architecture called Mamba can be more efficient than previous methods while still getting good results. The authors introduce three new models that combine ideas from transformers and state-space models. They test these models on several datasets and find that they work well, especially when the network is changing quickly. |
Keywords
» Artificial intelligence » Embedding » Transformer
