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arXiv Machine Learning By Lintao Yanga, Sirui Lia, Yaqing Wang, Pietro Li\`o, Xu Shen, Baisong Liu, Chengbin Peng

Node-level Graph Neural Architecture Search Framework

Read the original on arXiv Machine Learning →

The paper introduces N-GNAS, a Node-Level Graph Neural Architecture Search framework that selects distinct network architectures for different node subsets during feature updates. It incorporates a contrastive learning loss to better separate features across categories. Experiments on eight node and graph classification datasets show that N-GNAS outperforms existing GNAS methods and hand‑crafted GNNs, achieving 78.26% accuracy on CiteSeer.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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