# P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network

arXiv: https://arxiv.org/abs/2410.13905 · HTML: https://mlsanity.com/paper/2410.13905/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Secure Federated Matrix Factorization](https://mlsanity.com/paper/1906.05108/) (2019)
- [Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://mlsanity.com/paper/1708.03743/) (2017)
- [Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks](https://mlsanity.com/paper/1704.06803/) (2017)
- [DP-Forward: Fine-tuning and Inference on Language Models with Differential Privacy in Forward Pass](https://mlsanity.com/paper/2309.06746/) (2023)
- [GeniePath: Graph Neural Networks with Adaptive Receptive Paths](https://mlsanity.com/paper/1802.00910/) (2018)
- [A Survey of Graph Neural Networks for Social Recommender Systems](https://mlsanity.com/paper/2212.04481/) (2022)
- [Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification](https://mlsanity.com/paper/2005.11903/) (2020)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation](https://mlsanity.com/paper/2002.00844/) (2020)
- [A Vertical Federated Learning Framework for Graph Convolutional Network](https://mlsanity.com/paper/2106.11593/) (2021)
- [Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation](https://mlsanity.com/paper/2202.07253/) (2022)
- [Vertical Federated Graph Neural Network for Recommender System](https://mlsanity.com/paper/2303.05786/) (2023)
- [Robust Preference-Guided Denoising for Graph based Social Recommendation](https://mlsanity.com/paper/2303.08346/) (2023)
- [Conversation Modeling on Reddit using a Graph-Structured LSTM](https://mlsanity.com/paper/1704.02080/) (2017)
- [Secure Social Recommendation based on Secret Sharing](https://mlsanity.com/paper/2002.02088/) (2020)
- [Graph Bottlenecked Social Recommendation](https://mlsanity.com/paper/2406.08214/) (2024)
- [Improved Matrix Gaussian Mechanism for Differential Privacy](https://mlsanity.com/paper/2104.14808/) (2021)
- [Session-based Social and Dependency-aware Software Recommendation](https://mlsanity.com/paper/2103.06109/) (2021)

## Built on by (most cited first, 2 of 2)

- [UFGraphFR: Graph Federation Recommendation System based on User Text description features](https://mlsanity.com/paper/2501.08044/) (2025)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.