Modeling inter-channel dependencies is important for multivariate time series forecasting (MTSF). However, in many cases, inter-channel dependencies are time-varying and subject to noise interference, making it difficult for models to find a balance between structural stability and temporal adaptivity. Existing methods either use a single global static structure, resulting in insufficient sensitivity to temporal changes, or use local statistical correlations to construct dependencies, but local correlations are prone to introducing noise, which may further amplify the impact of noise during propagation. To address these issues, we propose a Constraint-Guided Dynamic Graph Network (ConDyGNet), whose core idea is "global basis, dynamic weights". Specifically, ConDyGNet learns a low-rank global basis as a shared structural constraint and generates patch-wise basis mixing weights to construct dynamic propagation graphs. This maintains topological consistency while allowing local adaptation and reducing the influence of local noise. We conducted extensive experiments on eight public benchmark datasets and multiple forecasting horizons, demonstrating that ConDyGNet can learn more robust time-varying inter-channel dependencies and achieve state-of-the-art forecasting accuracy. The code is available at https://github.com/constli67/ConDyGNet.