GripNet: Graph information propagation on supergraph for heterogeneous graphs

作者：

Highlights：

• A novel supergraph data structure to segregate a heterogeneous graph into interconnected, semantically-coherent subgraphs for efficient learning.

• A new graph representation learning framework based on supergraph for heterogeneous graphs and graph-based data integration.

• Extensive experiments on seven large-scale datasets for link prediction and node classification tasks.

摘要

•A novel supergraph data structure to segregate a heterogeneous graph into interconnected, semantically-coherent subgraphs for efficient learning.•A new graph representation learning framework based on supergraph for heterogeneous graphs and graph-based data integration.•Extensive experiments on seven large-scale datasets for link prediction and node classification tasks.

论文关键词：Graph representation learning,Heterogeneous graph,Data integration,Multi-relational link prediction,Node classification

论文评审过程：Received 18 May 2021, Revised 26 July 2022, Accepted 10 August 2022, Available online 12 August 2022, Version of Record 29 August 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108973