# Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

arXiv: https://arxiv.org/abs/2109.11898 · HTML: https://mlsanity.com/paper/2109.11898/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory](https://mlsanity.com/paper/2402.15326/) (2024)
- [HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2411.07022/) (2024)
- [TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information](https://mlsanity.com/paper/2608.30394/) (2026)

## Abstract (arXiv)

Social recommendation based on social network has achieved great success in improving the performance of recommendation system. Since social network (user-user relations) and user-item interactions are both naturally represented as graph-structured data, Graph Neural Networks (GNNs) have thus been widely applied for social recommendation. In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN aims to learn a heterogeneous global graph that makes full use of user-user relations, user-item interactions and item-item similarities in a unified perspective. To this end, we design a Graph Learner (GL) method to learn and optimize user-user and item-item connections separately. Moreover, we employ a Heterogeneous Graph Neural Network (HGNN) to capture the high-order complex semantic relations from our learned heterogeneous global graph. To scale up the computation of graph learning, we further present the Anchor-based Graph Learner (AGL) to reduce computational complexity. Extensive experiments on four real-world datasets demonstrate the effectiveness of our model.