# Score-based Generative Diffusion Models for Social Recommendations

arXiv: https://arxiv.org/abs/2412.15579 · HTML: https://mlsanity.com/paper/2412.15579/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion recommenders, one of 14 directions in Diffusion models.
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Placed in

- Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/)

## Builds on (most cited first, 30 of 33)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations](https://mlsanity.com/paper/2202.02514/) (2022)
- [Image Restoration with Mean-Reverting Stochastic Differential Equations](https://mlsanity.com/paper/2301.11699/) (2023)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Elucidating the Exposure Bias in Diffusion Models](https://mlsanity.com/paper/2308.15321/) (2023)
- [Diffusion Recommender Model](https://mlsanity.com/paper/2304.04971/) (2023)
- [Generative Diffusion Models on Graphs: Methods and Applications](https://mlsanity.com/paper/2302.02591/) (2023)
- [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://mlsanity.com/paper/2310.20453/) (2023)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [Blurring-Sharpening Process Models for Collaborative Filtering](https://mlsanity.com/paper/2211.09324/) (2022)
- [Graph Machine Learning in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2404.14928/) (2024)
- [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://mlsanity.com/paper/2106.03569/) (2021)
- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [Disentangled Contrastive Learning for Social Recommendation](https://mlsanity.com/paper/2208.08723/) (2022)
- [Graph Signal Diffusion Model for Collaborative Filtering](https://mlsanity.com/paper/2311.08744/) (2023)
- [Graph Unlearning with Efficient Partial Retraining](https://mlsanity.com/paper/2403.07353/) (2024)
- [RecDiff: Diffusion Model for Social Recommendation](https://mlsanity.com/paper/2406.01629/) (2024)
- [Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity](https://mlsanity.com/paper/2404.14240/) (2024)
- [Graph Collaborative Signals Denoising and Augmentation for Recommendation](https://mlsanity.com/paper/2304.03344/) (2023)
- [Robust Preference-Guided Denoising for Graph based Social Recommendation](https://mlsanity.com/paper/2303.08346/) (2023)
- [Modelling High-Order Social Relations for Item Recommendation](https://mlsanity.com/paper/2003.10149/) (2020)
- [Challenging Low Homophily in Social Recommendation](https://mlsanity.com/paper/2401.14606/) (2024)
- [Multi-agent Attacks for Black-box Social Recommendations](https://mlsanity.com/paper/2311.07127/) (2023)

## Built on by (most cited first, 23 of 23)

- [High-Order Matching for One-Step Shortcut Diffusion Models](https://mlsanity.com/paper/2502.00688/) (2025)
- [Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling](https://mlsanity.com/paper/2502.08150/) (2025)
- [Circuit Complexity Bounds for Visual Autoregressive Model](https://mlsanity.com/paper/2501.04299/) (2025)
- [On Computational Limits of FlowAR Models: Expressivity and Efficiency](https://mlsanity.com/paper/2502.16490/) (2025)
- [Theoretical Guarantees for High Order Trajectory Refinement in Generative Flows](https://mlsanity.com/paper/2503.09069/) (2025)
- [Fast RoPE Attention: Combining the Polynomial Method and Fast Fourier Transform](https://mlsanity.com/paper/2505.11892/) (2025)
- [Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendation](https://mlsanity.com/paper/2504.02458/) (2025)
- [Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse](https://mlsanity.com/paper/2505.16284/) (2025)
- [Exposing Privacy Risks in Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.17222/) (2025)
- [HOFAR: High-Order Augmentation of Flow Autoregressive Transformers](https://mlsanity.com/paper/2503.08032/) (2025)
- [Unlearning Inversion Attacks for Graph Neural Networks](https://mlsanity.com/paper/2506.00808/) (2025)
- [Learning Structure-Semantic Evolution Trajectories for Graph Domain Adaptation](https://mlsanity.com/paper/2602.10506/) (2026)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Graph Defense Diffusion Model](https://mlsanity.com/paper/2501.11568/) (2025)
- [A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations](https://mlsanity.com/paper/2508.14351/) (2025)
- [Geometric Flow Matching for Molecular Conformation Generation via Manifold Decomposition](https://mlsanity.com/paper/2605.25577/) (2026)
- [WebRec: Enhancing LLM-based Recommendations with Attention-guided RAG from Web](https://mlsanity.com/paper/2511.14182/) (2025)
- [Inference Attacks Against Graph Generative Diffusion Models](https://mlsanity.com/paper/2601.03701/) (2026)
- [Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks](https://mlsanity.com/paper/2603.18570/) (2026)
- [Fine-Tuning Diffusion-Based Recommender Systems via Reinforcement Learning with Reward Function Optimization](https://mlsanity.com/paper/2511.06937/) (2025)
- [The Confidence Trap: Calibration Attacks for Graph Neural Networks](https://mlsanity.com/paper/2606.08467/) (2026)
- [Diffusion Language Model for Recommendation](https://mlsanity.com/paper/2607.21519/) (2026)
- [PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks](https://mlsanity.com/paper/2608.21381/) (2026)

## Abstract (arXiv)

With the prevalence of social networks on online platforms, social recommendation has become a vital technique for enhancing personalized recommendations. The effectiveness of social recommendations largely relies on the social homophily assumption, which presumes that individuals with social connections often share similar preferences. However, this foundational premise has been recently challenged due to the inherent complexity and noise present in real-world social networks. In this paper, we tackle the low social homophily challenge from an innovative generative perspective, directly generating optimal user social representations that maximize consistency with collaborative signals. Specifically, we propose the Score-based Generative Model for Social Recommendation (SGSR), which effectively adapts the Stochastic Differential Equation (SDE)-based diffusion models for social recommendations. To better fit the recommendation context, SGSR employs a joint curriculum training strategy to mitigate challenges related to missing supervision signals and leverages self-supervised learning techniques to align knowledge across social and collaborative domains. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach in filtering redundant social information and improving recommendation performance.