Summary of Causal-aware Graph Neural Architecture Search Under Distribution Shifts, by Peiwen Li et al.
Causal-aware Graph Neural Architecture Search under Distribution Shifts
by Peiwen Li, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Jialong Wang, Yang Li, Wenwu Zhu
First submitted to arxiv on: 26 May 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 This paper proposes a novel approach called Causal-aware Graph Neural Architecture Search (CARNAS) to autonomously design GNN architectures that generalize well under distribution shifts. Existing graph NAS methods fail to generalize due to spurious correlations between graphs and architectures. To address this, CARNAS discovers the causal relationship between graphs and architectures using Disentangled Causal Subgraph Identification and Graph Embedding Intervention. This allows it to tailor generalized graph architectures using Invariant Architecture Customization. The proposed approach is evaluated through extensive experiments, demonstrating its ability to achieve advanced out-of-distribution generalization. Key contributions include the discovery of stable causal subgraphs across distributions and the customization of graph architectures for invariant prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to design computer models that can work well with different types of data. Right now, these models don’t do very well when they’re used with new or unusual data. The authors propose a method called CARNAS that helps the model learn the right relationships between the data and itself. This allows the model to adapt to new situations better. They test their approach and show it can handle unexpected changes in data more effectively than current methods. |
Keywords
» Artificial intelligence » Embedding » Generalization » Gnn
