Abstract: Social recommender systems explore collaborative data behind social labels to offer personalized suggestions. Despite many users lacking explicit social connections, they share similar item interaction histories. Prior studies have tried extracting high-order implicit social features via complex metapaths, which often diminishes model practicality. Additionally, the noise in these features can reduce adaptability when combined with explicit features using concatenation or deep learning. Recently, Generative Adversarial Networks (GANs) have supported robust data augmentation but pose challenges with model convergence, impacting efficiency in social recommendation contexts. To address these issues, we introduce AFS-GAN (adaptive fusion of social features in generative adversarial networks for recommender systems), a generative adversarial recommendation model with an adaptive fusion strategy for user social features. Initially, two simple metapaths extract first-order explicit and second-order implicit social features, improving model practicality and reducing subjective bias. An adaptive factor then flexibly fuses these features, capturing the diversity of user behaviors and enhancing recommendation adaptability. The generator uses Straight-through Gumbel Softmax to speed up pseudo-item generation, while the discriminator employs a quartic Bayesian Personalized Ranking (BPR) loss function to optimize discrimination loss, thus speeding convergence and simplifying the model. Extensive comparisons on 4 benchmark recommendation datasets with 8 leading social recommendation models have demonstrated that our approach excels in Precision, Recall, and NDCG.