Heterogeneous graph neural network based on dual-view graph structure augmentation

Jinjie Chen

doi:10.1117/12.3034755

11 July 2024 Heterogeneous graph neural network based on dual-view graph structure augmentation

Jinjie Chen

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132100L (2024) https://doi.org/10.1117/12.3034755
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Heterogeneous Graph Neural Networks (HGNNs) have emerged as powerful tools for handling heterogeneous graphs. However, current HGNNs often rely on meta-paths or intricate aggregation operations. In response, we introduce a heterogeneous graph neural network based on dual-view graph structure augmentation, which consists of three aggregation processes. By leveraging both node feature information and graph topology structure information, our method selecting homogeneous neighbors for nodes and constructing homogeneous views. Subsequently, it learns node representations through aggregation on these views and the original graph. Through extensive experiments on three widely used real-world heterogeneous graphs, our method demonstrates its simplicity and effectiveness, and outperforms the most of existing models in the task of heterogeneous graph node classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jinjie Chen "Heterogeneous graph neural network based on dual-view graph structure augmentation", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100L (11 July 2024); https://doi.org/10.1117/12.3034755

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