Owing　to　the　strength　in　learning　representation　of　the　high-order　connectivity　of　graph　neural　networks　(GNN),　GNN-based　collaborative　filtering　has　been　widely　adopted　in　recommender　systems.　Furthermore,　to　overcome　the　data　sparsity　problem,　some　recent　GNN-based　models　attempt　to　incorporate　social　information　and　to　design　contrastive　learning　as　an　auxiliary　task　to　assist　the　primary　recommendation　task.　Existing　GNN　and　contrastive-learning-based　recommendation　models　learn　user　and　item　representations　in　a　symmetrical　way　and　utilize　social　information　and　contrastive　learning　in　a　complex　manner.　The　above　two　strategies　lead　to　these　models　being　either　ineffective　for　datasets　with　a　serious　imbalance　between　users　and　items　or　inefficient　for　datasets　with　too　many　users　and　items.　In　this　work,　we　propose　a　contrastive　graph　learning　(CGL)　model,　which　combines　social　information　and　contrastive　learning　in　a　simple　and　powerful　way.　CGL　consists　of　three　modules:　diffusion,　readout,　and　prediction.　The　diffusion　module　recursively　aggregates　and　integrates　social　information　and　interest　information　to　learn　representations　of　users　and　items.　The　readout　module　takes　the　average　value　of　user　embeddings　from　all　diffusion　layers　and　item　embeddings　at　the　last　diffusion　layer　as　readouts　of　users　and　items,　respectively.　The　prediction　module　calculates　prediction　rating　scores　with　an　interest　graph　to　emphasize　interest　information.　Three　different　losses　are　designed　to　ensure　the　function　of　each　module.　Extensive　experiments　on　three　benchmark　datasets　are　implemented　to　validate　the　effectiveness　of　our　model.