  Graph Neural Networks (GNNs) have been extensively used for mining
graph-structured data with impressive performance. However, because these
traditional GNNs do not distinguish among various downstream tasks, embeddings
embedded by them are not always effective. Intuitively, paths in a graph imply
different semantics for different downstream tasks. Inspired by this, we design
a novel GNN solution, namely Customized Graph Neural Network with Path
Reweighting (CustomGNN for short). Specifically, the proposed CustomGNN can
automatically learn the high-level semantics for specific downstream tasks to
highlight semantically relevant paths as well to filter out task-irrelevant
noises in a graph. Furthermore, we empirically analyze the semantics learned by
CustomGNN and demonstrate its ability to avoid the three inherent problems in
traditional GNNs, i.e., over-smoothing, poor robustness, and overfitting. In
experiments with the node classification task, CustomGNN achieves
state-of-the-art accuracies on three standard graph datasets and four large
graph datasets. The source code of the proposed CustomGNN is available at
\url{https://github.com/cjpcool/CustomGNN}.
