Recently, sign-aware graph recommendations have drawn attention as they learn users’ negative preferences in addition to positive ones. Nevertheless, due to adopting two independent encoders for positive and negative interactions, existing approaches fail to learn the users’ comprehensive negative preferences and holistic collaborative signals from high-order heterogeneous interactions formed by multiple links with different signs. To compensate for this drawback, we devise a novel unified modeling approach to capture complete collaborative information and comprehensive user preferences. In this paper, we first explore the relationship between negative preferences and find that propagating both positive and negative high-order preferences along positive edges is feasible. Based on the observation, a Light Signed Graph Convolution Network for Recommendation (LSGRec) is proposed to comprehend user preferences within signed user-item interaction graphs. Then, recommendation results are generated based on positive preferences and optimized with negative ones. Finally, representations of users and items are trained through different auxiliary tasks. Extensive experiments on three real-world datasets demonstrate that our method outperforms existing baselines regarding performance and computational efficiency.