Title: DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation. Abstract: Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. 
Recently, deep generative models have shown a promising way of performing de-novo molecular design. 
Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.