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Deep graph transformation for attributed, directed, and signed networks
Knowledge and Information Systems ( IF 2.5 ) Pub Date : 2021-04-03 , DOI: 10.1007/s10115-021-01553-9
Xiaojie Guo , Liang Zhao , Houman Homayoun , Sai Manoj Pudukotai Dinakarrao

Generalized from image and language translation, the goal of graph translation or transformation is to generate a graph of the target domain on the condition of an input graph of the source domain. Existing works are limited to either merely generating the node attributes of graphs with fixed topology or only generating the graph topology without allowing the node attributes to change. They are prevented from simultaneously generating both node and edge attributes due to: (1) difficulty in modeling the iterative, interactive, and asynchronous process of both node and edge translation and (2) difficulty in learning and preserving the inherent consistency between the nodes and edges in generated graphs. A general, end-to-end framework for jointly generating node and edge attributes is needed for real-world problems. In this paper, this generic problem of multi-attributed graph translation is named and a novel framework coherently accommodating both node and edge translations is proposed. The proposed generic edge translation path is also proven to be a generalization of existing topology translation models. Then, in order to discover and preserve the consistency of the generated nodes and edges, a spectral graph regularization based on our nonparametric graph Laplacian is designed. In addition, two extensions of the proposed model are developed for signed and directed graph translation. Lastly, comprehensive experiments on both synthetic and real-world practical datasets demonstrate the power and efficiency of the proposed method.



中文翻译:

用于属性,定向和签名网络的深度图转换

从图像和语言翻译概括起来,图翻译或转换的目的是在源域的输入图的条件下生成目标域的图。现有工作仅限于仅生成具有固定拓扑的图的节点属性,或仅生成图拓扑而不允许更改节点属性。由于以下原因,阻止了它们同时生成节点和边缘属性:(1)难以对节点和边缘转换的迭代,交互和异步过程进行建模;(2)学习和保留节点之间固有的一致性困难;以及生成图形中的边。对于实际问题,需要一个通用的端到端框架来共同生成节点和边缘属性。在本文中,提出了一种多属性图转换的通用问题,并提出了一种新的框架,该框架一致地容纳节点和边缘转换。所提出的通用边缘转换路径也被证明是对现有拓扑转换模型的概括。然后,为了发现并保持生成的节点和边缘的一致性,设计了一种基于我们的非参数图拉普拉斯算子的光谱图正则化方法。此外,还针对符号和有向图转换开发了所提出模型的两个扩展。最后,在合成和真实世界的实际数据集上进行的综合实验证明了该方法的强大功能和效率。所提出的通用边缘转换路径也被证明是对现有拓扑转换模型的概括。然后,为了发现并保持生成的节点和边缘的一致性,设计了一种基于我们的非参数图拉普拉斯算子的光谱图正则化方法。此外,还针对符号和有向图转换开发了所提出模型的两个扩展。最后,在合成和真实世界的实际数据集上进行的综合实验证明了该方法的强大功能和效率。所提出的通用边缘转换路径也被证明是对现有拓扑转换模型的概括。然后,为了发现并保持生成的节点和边缘的一致性,设计了一种基于我们的非参数图拉普拉斯算子的光谱图正则化方法。此外,还针对符号和有向图转换开发了所提出模型的两个扩展。最后,在合成和真实世界的实际数据集上进行的综合实验证明了该方法的强大功能和效率。此外,还针对符号和有向图转换开发了所提出模型的两个扩展。最后,在合成和真实世界的实际数据集上进行的综合实验证明了该方法的强大功能和效率。此外,还针对符号和有向图转换开发了所提出模型的两个扩展。最后,在合成和真实世界的实际数据集上进行的综合实验证明了该方法的强大功能和效率。

更新日期:2021-04-04
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