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.

翻译：近年来，基于深度学习的方法已被用于建模和生成服从不同分布的图结构。然而，这些方法通常属于无监督学习和无条件生成模型，或仅以图级上下文为条件，缺乏与丰富的语义节点级上下文的关联。本文关注一个名为"条件时序图生成"的新问题：给定输入多元时间序列，我们旨在推断一个目标关系图，该图建模时间序列间的潜在相互关系，其中每个节点对应一个时间序列。例如，我们可以基于以时间序列形式记录的基因表达数据，研究某种疾病的基因调控网络中基因间的相互关系。为实现这一目标，我们提出了一种新型的条件时序图生成-生成对抗网络（TSGG-GAN），以应对丰富的节点级上下文结构条件化以及直接衡量图与时间序列间相似性的挑战。在合成数据集和真实基因调控网络数据集上的大量实验表明，所提出的TSGG-GAN具有有效性和泛化能力。