  Recently, Graph Neural Networks (GNNs) have greatly advanced the task of
graph classification. Typically, we first build a unified GNN model with graphs
in a given training set and then use this unified model to predict labels of
all the unseen graphs in the test set. However, graphs in the same dataset
often have dramatically distinct structures, which indicates that a unified
model may be sub-optimal given an individual graph. Therefore, in this paper,
we aim to develop customized graph neural networks for graph classification.
Specifically, we propose a novel customized graph neural network framework,
i.e., Customized-GNN. Given a graph sample, Customized-GNN can generate a
sample-specific model for this graph based on its structure. Meanwhile, the
proposed framework is very general that can be applied to numerous existing
graph neural network models. Comprehensive experiments on various graph
classification benchmarks demonstrate the effectiveness of the proposed
framework.
