Node-level Graph Neural Architecture Search Framework
To overcome this limitation, in this work, we propose a Node-Level Graph Neural Architecture Search (N-GNAS) algorithm.
Key points
- In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data.
- Nevertheless, traditional approaches typically apply uniform convolution operations to all nodes, regardless of their varying structural and feature characteristics, which can undermine model performance and result in over-smoothing issues as the number of layers increases.
- N-GNAS also introduces a contrastive learning loss to separate sample features from different categories and vice versa.
- In experiments conducted on eight datasets for node and graph classification, our methodology outperforms current leading GNAS techniques and traditional human-designed GNNs.
Sources (1)
- [1]Node-level Graph Neural Architecture Search FrameworkarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 01:58 AM
To overcome this limitation, in this work, we propose a Node-Level Graph Neural Architecture Search (N-GNAS) algorithm.
In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data.
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