This paper proposes GraphPro, a short-term link speed prediction framework for signalized urban traffic networks. Different from other traditional approaches that adopt only reactive inputs (i.e., surrounding traffic data), GraphPro also accepts proactive inputs (i.e., traffic signal timing). This allows GraphPro to predict link speed more accurately, depending on whether or not there is a contextual change in traffic signal timing. A Wasserstein generative adversarial network (WGAN) structure, including a generator (prediction model) and a discriminator, is employed to incorporate unprecedented network traffic states and ensures a high level of generalizability for the prediction model. A hybrid graph block, comprised of a reactive cell and a proactive cell, is implemented into each neural layer of the generator. In order to jointly capture spatio-temporal influences and signal contextual information on traffic links, the two cells adopt several key neural network-based components, including graph convolutional network, recurrent neural architecture, and self-attention mechanism. The double-cell structure ensures GraphPro learns from proactive input only when required. The effectiveness and efficiency of GraphPro are tested on a short-term link speed prediction task using real-world traffic data. Due to the capabilities of learning from real data distribution and generating unseen samples, GraphPro offers a more reliable and robust prediction when compared with state-of-the-art data-driven models.