  Deep generative models for graphs have exhibited promising performance in
ever-increasing domains such as design of molecules (i.e, graph of atoms) and
structure prediction of proteins (i.e., graph of amino acids). Existing work
typically focuses on static rather than dynamic graphs, which are actually very
important in the applications such as protein folding, molecule reactions, and
human mobility. Extending existing deep generative models from static to
dynamic graphs is a challenging task, which requires to handle the
factorization of static and dynamic characteristics as well as mutual
interactions among node and edge patterns. Here, this paper proposes a novel
framework of factorized deep generative models to achieve interpretable dynamic
graph generation. Various generative models are proposed to characterize
conditional independence among node, edge, static, and dynamic factors. Then,
variational optimization strategies as well as dynamic graph decoders are
proposed based on newly designed factorized variational autoencoders and
recurrent graph deconvolutions. Extensive experiments on multiple datasets
demonstrate the effectiveness of the proposed models.
