  Graph Neural Networks (GNNs) have recently shown to be powerful tools for
representing and analyzing graph data. So far GNNs is becoming an increasingly
critical role in software engineering including program analysis, type
inference, and code representation. In this paper, we introduce GraphGallery, a
platform for fast benchmarking and easy development of GNNs based software.
GraphGallery is an easy-to-use platform that allows developers to automatically
deploy GNNs even with less domain-specific knowledge. It offers a set of
implementations of common GNN models based on mainstream deep learning
frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be
easily incorporated into the platform. Experiments demonstrate the reliability
of implementations and superiority in fast coding. The official source code of
GraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a
demo video can be found at https://youtu.be/mv7Zs1YeaYo.
