Recently deep generative models for static networks have been under active development and achieved significant success in application areas such as molecule design. However, many real-world problems involve temporal graphs whose topology and attribute values evolve dynamically over time, such as in the cases of protein folding, human mobility networks, and social network growth. However, deep generative models for temporal graphs has rarely been well explored yet and existing techniques for static graphs are not up to the task for temporal graphs since they cannot 1) encode and decode continuously-varying graph topology chronologically, 2) enforce validity via temporal constraints, and 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 ourperforms the comparison methods in efficiency and effectiveness.

我们提出了一种名为'时间图生成对抗网络'的新模型，它能够连续地生成时间图，并针对回归神经网络设计了一些新的激活函数，以强制实施时间合法性约束，以及一种新的时间图鉴别器，以提高效率和准确性。

TG-GAN: 基于深度生成模型的连续时间时序图形生成