  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.
