Abstract
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing methods either capture semantic relationships but indirectly leverage node/edge attributes in a complex way, or leverage node/edge attributes directly without taking semantic relationships into account. When involving multiple convolution operations, they also have poor scalability. To overcome these limitations, this paper proposes a flexible and efficient Graph information propagation Network (GripNet) framework. Specifically, we introduce a new supergraph data structure consisting of supervertices and superedges. A supervertex is a semantically-coherent subgraph. A superedge defines an information propagation path between two supervertices. GripNet learns new representations for the supervertex of interest by propagating information along the defined path using multiple layers. We construct multiple large-scale graphs and evaluate GripNet against competing methods to show its superiority in link prediction, node classification, and data integration.
Hao Xu
MSc Student (now a PhD student at UCSD)
Peizhen Bai
PhD Student (now a Senior Machine Learning Scientist at AstraZeneca)
Haiping Lu
Director of the UK Open Multimodal AI Network, Professor of Machine Learning, and Head of AI Research Engineering
I am a Professor of Machine Learning. I develop translational multimodal AI technologies for advancing healthcare and scientific discovery.