Summary of Sa-gnas: Seed Architecture Expansion For Efficient Large-scale Graph Neural Architecture Search, by Guanghui Zhu et al.
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
by Guanghui Zhu, Zipeng Ji, Jingyan Chen, Limin Wang, Chunfeng Yuan, Yihua Huang
First submitted to arxiv on: 3 Dec 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 Medium Difficulty summary: The paper proposes SA-GNAS, a novel framework for efficient large-scale Graph Neural Architecture Search (GNAS). Current GNAS methods struggle to handle graphs with millions of nodes and edges due to computational overhead. SA-GNAS addresses this issue by iteratively expanding a seed architecture using performance ranking consistency-based selection and entropy minimization. The proposed method outperforms human-designed state-of-the-art GNN architectures and existing graph NAS methods on five large-scale graphs, while reducing search time by up to 2.8 times compared to GAUSS. SA-GNAS is parallelizable and can be further improved with more GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper introduces a new way to design computer programs that work with complex networks of data called graph neural architectures. The current methods for doing this are slow and not very efficient when dealing with huge networks. To solve this problem, the researchers propose a new approach called SA-GNAS. It starts by creating a simple architecture and then gradually builds upon it to make it better. This method is much faster than previous ones and can handle massive networks. The results show that SA-GNAS outperforms other existing methods on large-scale graphs. |
Keywords
* Artificial intelligence * Gnn
