Pooling Architecture Search for Graph Classification

• URL: http://arxiv.org/abs/2108.10587v1
• Date: Tue, 24 Aug 2021 09:03:03 GMT
• Title: Pooling Architecture Search for Graph Classification
• Authors: Lanning Wei, Huan Zhao, Quanming Yao, Zhiqiang He
• Abstract summary: Graph neural networks (GNNs) are designed to learn node-level representation based on neighborhood aggregation schemes. Pooling methods are applied after the aggregation operation to generate coarse-grained graphs. It is a challenging problem to design a universal pooling architecture to perform well in most cases. We propose to use neural architecture search (NAS) to search for adaptive pooling architectures for graph classification.
• Score: 36.728077433219916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. GNNs are designed to learn node-level representation based on neighborhood aggregation schemes, and to obtain graph-level representation, pooling methods are applied after the aggregation operation in existing GNN models to generate coarse-grained graphs. However,due to highly diverse applications of graph classification, and the performance of existing pooling methods vary on different graphs. In other words, it is a challenging problem to design a universal pooling architecture to perform well in most cases, leading to a demand for data-specific pooling methods in real-world applications. To address this problem, we propose to use neural architecture search (NAS) to search for adaptive pooling architectures for graph classification. Firstly we designed a unified framework consisting of four modules: Aggregation, Pooling, Readout, and Merge, which can cover existing human-designed pooling methods for graph classification. Based on this framework, a novel search space is designed by incorporating popular operations in human-designed architectures. Then to enable efficient search, a coarsening strategy is proposed to continuously relax the search space, thus a differentiable search method can be adopted. Extensive experiments on six real-world datasets from three domains are conducted, and the results demonstrate the effectiveness and efficiency of the proposed framework.

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