OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network
Hui Han, Tianyu Zhao, Cheng Hong Yang, Zhang Hongyi, Yaoqi Liu, Xiao Wang, Chuan Shi · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph representation learning methods on heterogeneous graphs, have attracted increasing attention of many researchers. Although, several existing libraries have supported HGNNs, they just provide the most basic models and operators. Building and benchmarking various downstream tasks on HGNNs is still painful and time consuming with them. In this paper, we will introduce OpenHGNN, an open-source toolkit for HGNNs. OpenHGNN defines a unified and standard pipeline for training and testing, which can allow users to run a model on a specific dataset with just one command line. OpenHGNN has integrated 20+ mainstream HGNNs and 20+ heterogeneous graph datasets, which can be used for various advanced tasks, such as node classification, link prediction, and recommendation. In addition, thanks to the modularized design of OpenHGNN, it can be extended to meet users' customized needs. We also release several novel and useful tools and features, including leaderboard, autoML, design space, and visualization, to provide users with better usage experiences. OpenHGNN is an open-source project, and the source code is available at https://github.com/BUPT-GAMMA/OpenHGNN.