# Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

arXiv: https://arxiv.org/abs/2110.04039 · HTML: https://mlsanity.com/paper/2110.04039/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Session-based Social Recommendation via Dynamic Graph Attention Networks](https://mlsanity.com/paper/1902.09362/) (2019)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Deep Social Collaborative Filtering](https://mlsanity.com/paper/1907.06853/) (2019)
- [Enhancing Social Recommendation with Adversarial Graph Convolutional Networks](https://mlsanity.com/paper/2004.02340/) (2020)
- [SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation](https://mlsanity.com/paper/1811.02815/) (2018)
- [Salience and Market-aware Skill Extraction for Job Targeting](https://mlsanity.com/paper/2005.13094/) (2020)
- [Quaternion Collaborative Filtering for Recommendation](https://mlsanity.com/paper/1906.02594/) (2019)
- [Generating Reliable Friends via Adversarial Training to Improve Social Recommendation](https://mlsanity.com/paper/1909.03529/) (2019)

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

- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications](https://mlsanity.com/paper/2110.08760/) (2021)
- [Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling](https://mlsanity.com/paper/2201.02307/) (2022)

## Abstract (arXiv)

Social recommendation which aims to leverage social connections among users to enhance the recommendation performance. With the revival of deep learning techniques, many efforts have been devoted to developing various neural network-based social recommender systems, such as attention mechanisms and graph-based message passing frameworks. However, two important challenges have not been well addressed yet: (i) Most of existing social recommendation models fail to fully explore the multi-type user-item interactive behavior as well as the underlying cross-relational inter-dependencies. (ii) While the learned social state vector is able to model pair-wise user dependencies, it still has limited representation capacity in capturing the global social context across users. To tackle these limitations, we propose a new Social Recommendation framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we first design a relation-aware reconstructed graph neural network to inject the cross-type collaborative semantics into the recommendation framework. In addition, we further augment SR-HGNN with a social relation encoder based on the mutual information learning paradigm between low-level user embeddings and high-level global representation, which endows SR-HGNN with the capability of capturing the global social contextual signals. Empirical results on three public benchmarks demonstrate that SR-HGNN significantly outperforms state-of-the-art recommendation methods. Source codes are available at: https://github.com/xhcdream/SR-HGNN.