Deep Graph Translation
Deep Graph Translation
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DOI：
10.1109/tnnls.2022.3144670
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发表时间：
2018-05
影响因子：
10.4
通讯作者：
Xiaojie Guo;Lingfei Wu;Liang Zhao
中科院分区：
文献类型：
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作者：
Xiaojie Guo;Lingfei Wu;Liang Zhao
图的深度生成模型最近在建模和生成用于研究生物学、工程和社会科学网络的图方面取得了巨大成功。然而，它们通常是无条件的生成模型，无法控制给定源图的目标图。在本文中，我们提出了一种新颖的图翻译生成对抗网络（GT-GAN）模型，可将源图转换为其目标输出图。 GT-GAN 由配备创新图卷积和反卷积层的图翻译器组成，用于学习考虑全局和局部特征的翻译映射。提出了一种新的条件图鉴别器，通过在训练时对源图进行条件化来对目标图进行分类。对网络网络、物联网和神经科学领域的多个合成和现实数据集进行的广泛实验表明，所提出的 GT-GAN 模型在有效性和可扩展性方面显着优于其他基线方法。例如，GT-GAN 在大脑网络的功能连接 (FC) 预测方面优于经典的最先进 (SOTA) 方法至少 32.5%。
Deep generative models for graphs have recently achieved great successes in modeling and generating graphs for studying networks in biology, engineering, and social sciences. However, they are typically unconditioned generative models that have no control over the target graphs given a source graph. In this article, we propose a novel graph-translation-generative-adversarial-nets (GT-GAN) model that transforms the source graphs into their target output graphs. GT-GAN consists of a graph translator equipped with innovative graph convolution and deconvolution layers to learn the translation mapping considering both global and local features. A new conditional graph discriminator is proposed to classify the target graphs by conditioning on source graphs while training. Extensive experiments on multiple synthetic and real-world datasets in the domain of cybernetworks, the Internet of Things, and neuroscience demonstrate that the proposed GT-GAN model significantly outperforms other baseline methods in terms of both effectiveness and scalability. For instance, GT-GAN outperforms the classical state-of-the-art (SOTA) methods in functional connectivity (FC) prediction of brain networks by at least 32.5%.