  The recent deep generative models for static graphs that are now being
actively developed have achieved significant success in areas such as molecule
design. However, many real-world problems involve temporal graphs whose
topology and attribute values evolve dynamically over time, including important
applications such as protein folding, human mobility networks, and social
network growth. As yet, deep generative models for temporal graphs are not yet
well understood and existing techniques for static graphs are not adequate for
temporal graphs since they cannot 1) encode and decode continuously-varying
graph topology chronologically, 2) enforce validity via temporal constraints,
or 3) ensure efficiency for information-lossless temporal resolution. To
address these challenges, we propose a new model, called ``Temporal Graph
Generative Adversarial Network'' (TG-GAN) for continuous-time temporal graph
generation, by modeling the deep generative process for truncated temporal
random walks and their compositions. Specifically, we first propose a novel
temporal graph generator that jointly model truncated edge sequences, time
budgets, and node attributes, with novel activation functions that enforce
temporal validity constraints under recurrent architecture. In addition, a new
temporal graph discriminator is proposed, which combines time and node encoding
operations over a recurrent architecture to distinguish the generated sequences
from the real ones sampled by a newly-developed truncated temporal random walk
sampler. Extensive experiments on both synthetic and real-world datasets
demonstrate TG-GAN significantly outperforms the comparison methods in
efficiency and effectiveness.
