Search to aggregate neighborhood for graph neural network
This work addresses the computational bottlenecks in NAS for GNNs, offering a more efficient method for researchers and practitioners in graph-based machine learning.
The paper tackles the challenge of applying neural architecture search (NAS) to graph neural networks (GNNs) by proposing SANE, a framework that automatically designs data-specific GNN architectures, achieving superior effectiveness and efficiency on four tasks and seven datasets.
Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. To obtain data-specific GNN architectures, researchers turn to neural architecture search (NAS), which has made impressive success in discovering effective architectures in convolutional neural networks. However, it is non-trivial to apply NAS approaches to GNN due to challenges in search space design and the expensive searching cost of existing NAS methods. In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE), to automatically design data-specific GNN architectures. By designing a novel and expressive search space, we propose a differentiable search algorithm, which is more efficient than previous reinforcement learning based methods. Experimental results on four tasks and seven real-world datasets demonstrate the superiority of SANE compared to existing GNN models and NAS approaches in terms of effectiveness and efficiency. (Code is available at: https://github.com/AutoML-4Paradigm/SANE).