论文标题
GraphFederator：多方图的联合视觉分析
GraphFederator: Federated Visual Analysis for Multi-party Graphs
论文作者
论文摘要
本文介绍了GraphFederator，这是一种构建多方图形联合表示的新方法，并支持图形隐私的视觉分析。受联邦学习概念的启发，我们将多方图表的分析重新制定为分散过程。新的联邦框架由负责关节建模和分析的共享模块以及一组在各自的图形数据上运行的本地模块。具体而言，我们提出了一个联合图表模型（FGRM），该模型是从本地模块中多方图的加密特性中学到的。我们还设计了多方可视化，探索和分析多方图形的多个可视化视图。两个数据集的实验结果证明了我们方法的有效性。
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs. Inspired by the concept of federated learning, we reformulate the analysis of multi-party graphs into a decentralization process. The new federation framework consists of a shared module that is responsible for joint modeling and analysis, and a set of local modules that run on respective graph data. Specifically, we propose a federated graph representation model (FGRM) that is learned from encrypted characteristics of multi-party graphs in local modules. We also design multiple visualization views for joint visualization, exploration, and analysis of multi-party graphs. Experimental results with two datasets demonstrate the effectiveness of our approach.
