Related papers: GraphPAS: Parallel Architecture Search for Graph Neural Networks

GraphPAS: Parallel Architecture Search for Graph Neural Networks

URL: http://arxiv.org/abs/2112.03461v1
Date: Tue, 7 Dec 2021 02:55:24 GMT
Title: GraphPAS: Parallel Architecture Search for Graph Neural Networks
Authors: Jiamin Chen, Jianliang Gao, Yibo Chen, Oloulade Babatounde Moctard, Tengfei Lyu, Zhao Li
Abstract summary: We propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.
Score: 12.860313120881996
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Graph neural architecture search has received a lot of attention as Graph Neural Networks (GNNs) has been successfully applied on the non-Euclidean data recently. However, exploring all possible GNNs architectures in the huge search space is too time-consuming or impossible for big graph data. In this paper, we propose a parallel graph architecture search (GraphPAS) framework for graph neural networks. In GraphPAS, we explore the search space in parallel by designing a sharing-based evolution learning, which can improve the search efficiency without losing the accuracy. Additionally, architecture information entropy is adopted dynamically for mutation selection probability, which can reduce space exploration. The experimental result shows that GraphPAS outperforms state-of-art models with efficiency and accuracy simultaneously.

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