Mar 8, 2024 · We propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing ...
The authors propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures ...
We introduce three novel modules, i) disentangled graph architecture super-network, ii) self- supervised training with joint architecture-graph disentanglement ...
May 30, 2024 · To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to ...
Mar 8, 2024 · In this paper, we study unsupervised graph neural architecture search, ie, discovering optimal GNN architectures without labels for graph-structured data.
As supervised labels are not available in unsupervised settings, for fair comparisons, all the methods adopt the same self-supervised tasks, [5] and [6] for ...
This is a paper collection about automated graph learning, ie, fusing AutoML and graph learning. Two special focuses are graph hyper-parameter optimization ( ...
Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision. NeurIPS, 2023. (CCF-A) (Paper) (Code); [C23] New! Yijian Qin, Xin Wang ...
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision. Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao Shen, Shiqi Shen, and Wenwu ...
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Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision. Z Zhang, X Wang, Z Zhang, G Shen, S Shen, W Zhu. Thirty-Seventh Conference ...