Title: DEEP GRAPH TRANSLATION. Abstract: The tremendous success of deep generative models on generating continuous data
like image and audio has been achieved; however, few deep graph generative models
have been proposed to generate discrete data such as graphs. The recently proposed
approaches are typically unconditioned generative models which have no
control over modes of the graphs being generated. Differently, in this paper, we
are interested in a new problem named Deep Graph Translation: given an input
graph, the goal is to infer a target graph by learning their underlying translation
mapping. Graph translation could be highly desirable in many applications such
as disaster management and rare event forecasting, where the rare and abnormal
graph patterns (e.g., traffic congestions and terrorism events) will be inferred prior
to their occurrence even without historical data on the abnormal patterns for this
specific graph (e.g., a road network or human contact network). To this end, we
propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN)
which translates one mode of the input graphs to its target mode. GT-GAN consists
of a graph translator where we propose new graph convolution and deconvolution
layers to learn the global and local translation mapping. A new conditional
graph discriminator has also been proposed to classify target graphs by conditioning
on input graphs. Extensive experiments on multiple synthetic and real-world
datasets demonstrate the effectiveness and scalability of the proposed GT-GAN.