TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
TG-GAN: Continuous-time Temporal Graph Generation with Deep Generative Models
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发表时间：
2020
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通讯作者：
Liming Zhang;Liang Zhao;Shan Qin;D. Pfoser
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作者：
Liming Zhang;Liang Zhao;Shan Qin;D. Pfoser
最近正在积极开发的静态图形的深度生成模型在分子设计等领域取得了重大成功。然而，许多现实世界的问题涉及时间图，其拓扑结构和属性值随时间动态演变，包括蛋白质折叠、人类移动网络和社会网络增长等重要应用。到目前为止，时间图的深度生成模型还没有得到很好的理解，现有的静态图技术还不足以用于时间图，因为它们不能1)按时间顺序编码和解码连续变化的图拓扑，2)通过时间约束强制有效性，或3)确保信息无损时间分辨率的效率。为了解决这些挑战，我们提出了一个新的模型，称为“时间图生成对抗网络”（TG-GAN），用于连续时间时间图生成，通过建模截断时间随机游走及其组成的深度生成过程。具体来说，我们首先提出了一种新的时间图生成器，它联合建模截断的边缘序列、时间预算和节点属性，并使用新的激活函数在循环架构下强制执行时间有效性约束。此外，提出了一种新的时间图鉴别器，该鉴别器在循环结构上结合时间和节点编码操作，以区分生成的序列与新开发的截断时间随机漫步采样器采样的真实序列。在合成和真实数据集上进行的大量实验表明，TG-GAN在效率和有效性方面明显优于比较方法。
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.