Text generation is a fundamental and important task in natural language processing.Most of the existing models generate text in a sequential manner and have difficulty modeling complex dependency structures.In this paper,we treat the text generation task as a graph generation problem exploiting both syntactic and word-ordering relationships.Leveraging the framework of the graph neural network,we propose the word graph model.During the process,the model builds a sentence incrementally and maintains syntactic integrity via a syntax-driven,top-down,breadth-first generation process.Experimental results on both synthetic and real text generation tasks show the efficacy of our approach.