基于图神经网络的社交推荐算法利用社交网络提升推荐系统的性能。但是现有算法大多直接将原始社交图整合到推荐系统中，忽略了社交网络中存在的非同质性社交连接，从而为推荐系统引入噪声信息。此外，现有负采样策略选择固定难度的负样本，容易产生假负样本，导致模型对用户偏好的区分度不足。为解决上述问题，该文提出了一种基于社交扩散和自适应负采样的推荐算法。首先，对社交网络执行前向扩散和用户兴趣引导去噪操作，生成用户的同质性社交表示；然后利用多视图表征对齐方法，以最大化用户表示在去噪社交图、原始社交图和用户-项目交互图间的互信息，进而优化用户表示质量；最后，根据正样本预测评分选择自适应难度的负样本，实现正负样本相似度边界的动态校准，以提升模型的整体性能。实验结果表明，该算法较当前先进推荐算法效果显著，在数据集Douban上的召回率和归一化折扣累积增益分别提升了11.99%和10.54%，在数据集Epinions上分别提升了15.62%和11.14%，在数据集Yelp上分别提升了13.80%和14.90%，验证了其能有效缓解噪声干扰，区分正负样本之间的细微差别。

Social recommendation algorithms based on Graph Neural Network (GNN) leverage social networks to improve recommendation performance. However, most existing methods directly integrate the raw social graph into the recommendation system, which often introduces noise as they overlook the presence of non-homophilous social connections. Furthermore, prevailing negative sampling strategies typically select negative samples with a fixed level of hardness, which is prone to generating false negatives and consequently limits the model’s ability to effectively discriminate between user preferences. To address these issues, this paper proposed a novel recommendation algorithm based on social diffusion and adaptive negative sampling. First, forward diffusion and interest-guided denoising were performed on the social network to derive user representations that reflect homophilic social relations. Subsequently, a multi-view representation alignment approach was employed to maximize the mutual information among user representations from the denoised social graph, the original social graph, and the user-item interaction graph, thereby enhancing the quality of user embeddings. Finally, negative samples of adaptive hardness were selected based on the predicted scores of positive samples, enabling dynamic calibration of the similarity boundary between positive and negative pairs to improve overall model performance. Extensive experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art recommendation baselines. On the Douban dataset, it improves recall and NDCG by 11.99% and 10.54%, respectively; on Epinions, by 15.62% and 11.14%; and on Yelp, by 13.80% and 14.90%. These results validate its effectiveness in alleviating social noise and enhancing the differentiation between positive and negative samples.