Social recommender system assumes that user’s preferences can be influenced by their social connections. However, social networks are inherently noisy and contain redundant signals that are not helpful or even harmful for the recommendation task. In this extended abstract, we classify the noise in the explicit social links into intrinsic noise and extrinsic noise. Intrinsic noises are those edges that are natural in the social network but do not have an influence on the user preference modeling; Extrinsic noises, on the other hand, are those social links that are introduced intentionally through malicious attacks such that the attackers can manipulate the social influence to bias the recommendation outcome. To tackle this issue, we first propose a denoising framework that utilizes the information bottleneck principle and contrastive learning to filter out the noisy social edges and use the edges that are socially influential to enhance item prediction. Experiments will be conducted on the real-world datasets for the Top-K ranking evaluation as well as the model’s robustness to simulated social noises. Finally, we discuss the future plan about how to defend against extrinsic noise, which results from the malicious attack.