Recent advancements in graph representation learning have led to a focus on dynamic graphs.There is a need for generative models that can handle continuously changing temporal graph data.In this work, a new approach called DG-Gen is proposed to model interactions in dynamic graphs using joint probabilities.DG-Gen outperforms traditional methods in generating high-fidelity graphs and improving link prediction tasks.