Previous　recommendation　models　build　interest　embeddings　heavily　relying　on　the　observed　interactions　and　optimize　the　embeddings　with　a　contrast　between　the　interactions　and　randomly　sampled　negative　instances.　To　our　knowledge,　the　negative　interest　signals　remain　unexplored　in　interest　encoding,　which　merely　serves　losses　for　backpropagation.　Besides,　the　sparse　undifferentiated　interactions　inherently　bring　implicit　bias　in　revealing　users’　interests,　leading　to　suboptimal　interest　prediction.　The　negative　interest　signals　would　be　a　piece　of　promising　evidence　to　support　detailed　interest　modeling.　In　this　work,　we　propose　a　perturbed　graph　contrastive　learning　with　negative　propagation　(PCNP)　for　recommendation,　which　introduces　negative　interest　to　assist　interest　modeling　in　a　contrastive　learning　(CL)　architecture.　An　auxiliary　channel　of　negative　interest　learning　generates　a　contrastive　graph　by　negative　sampling　and　propagates　complementary　embeddings　of　users　and　items　to　encode　negative　signals.　The　proposed　PCNP　contrasts　positive　and　negative　embeddings　to　promote　interest　modeling　for　recommendation.　Extensive　experiments　demonstrate　the　capability　of　PCNP　using　two-level　CL　to　alleviate　interaction　sparsity　and　bias　issues　for　recommendation.　IEEE