  Deep learning based approaches have been utilized to model and generate
graphs subjected to different distributions recently. However, they are
typically unsupervised learning based and unconditioned generative models or
simply conditioned on the graph-level contexts, which are not associated with
rich semantic node-level contexts. Differently, in this paper, we are
interested in a novel problem named Time Series Conditioned Graph Generation:
given an input multivariate time series, we aim to infer a target relation
graph modeling the underlying interrelationships between time series with each
node corresponding to each time series. For example, we can study the
interrelationships between genes in a gene regulatory network of a certain
disease conditioned on their gene expression data recorded as time series. To
achieve this, we propose a novel Time Series conditioned Graph
Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of
rich node-level context structures conditioning and measuring similarities
directly between graphs and time series. Extensive experiments on synthetic and
real-word gene regulatory networks datasets demonstrate the effectiveness and
generalizability of the proposed TSGG-GAN.
