  As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have facilitated various applications from drug discovery to
recommender systems. Nevertheless, the effectiveness of GNNs is immensely
challenged by issues related to data quality, such as distribution shift,
abnormal features and adversarial attacks. Recent efforts have been made on
tackling these issues from a modeling perspective which requires additional
cost of changing model architectures or re-training model parameters. In this
work, we provide a data-centric view to tackle these issues and propose a graph
transformation framework named GTrans which adapts and refines graph data at
test time to achieve better performance. We provide theoretical analysis on the
design of the framework and discuss why adapting graph data works better than
adapting the model. Extensive experiments have demonstrated the effectiveness
of GTrans on three distinct scenarios for eight benchmark datasets where
suboptimal data is presented. Remarkably, GTrans performs the best in most
cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on
three experimental settings. Code is released at
https://github.com/ChandlerBang/GTrans.
