Abstract
It is challenging to build a Heterogeneous Graph (HG) representation learning model because HG is heterogeneous, irregular, and sparse. An easy-to-use and friendly framework is important for a beginner to make an understanding and get deep into this field. In this chapter, we are going to introduce OpenHGNN, a toolkit that can help to build HG models in a predesigned pipeline. And we will present the procedures with three well-known HG models that are HAN, the model first introduces attention mechanism to heterogeneous graph neural networks, RGCN, a model used to model multi-relational graphs with GCN, and HERec, a heterogeneous graph embedding method for recommendation.
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Shi, C., Wang, X., Yu, P.S. (2022). Platforms and Practice of Heterogeneous Graph Representation Learning. In: Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-16-6166-2_10
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DOI: https://doi.org/10.1007/978-981-16-6166-2_10
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