Simplifying Architecture Search for Graph Neural Network

• URL: http://arxiv.org/abs/2008.11652v2
• Date: Sun, 6 Sep 2020 12:06:14 GMT
• Title: Simplifying Architecture Search for Graph Neural Network
• Authors: Huan Zhao and Lanning Wei and Quanming Yao
• Abstract summary: We propose SNAG framework, consisting of a novel search space and a reinforcement learning based search algorithm. Experiments on real-world datasets demonstrate the effectiveness of SNAG framework compared to human-designed GNNs and NAS methods.
• Score: 38.45540097927176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural architecture search (NAS) methods, which have made impressive progress in discovering effective architectures in convolutional neural networks. Two preliminary works, GraphNAS and Auto-GNN, have made first attempt to apply NAS methods to GNN. Despite the promising results, there are several drawbacks in expressive capability and search efficiency of GraphNAS and Auto-GNN due to the designed search space. To overcome these drawbacks, we propose the SNAG framework (Simplified Neural Architecture search for Graph neural networks), consisting of a novel search space and a reinforcement learning based search algorithm. Extensive experiments on real-world datasets demonstrate the effectiveness of the SNAG framework compared to human-designed GNNs and NAS methods, including GraphNAS and Auto-GNN.

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