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arXiv · 2008.11989

GraphFederator: Federated Visual Analysis for Multi-party Graphs

Dongming Han·Wei Chen·Rusheng Pan·Yijing Liu·Jiehui Zhou·Ying Xu·Tianye Zhang·Changjie Fan·Jianrong Tao·Xiaolong·Zhang
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Abstract

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.

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Dongming Han, Wei Chen, Rusheng Pan, Yijing Liu, Jiehui Zhou, Ying Xu, Tianye Zhang, Changjie Fan, Jianrong Tao, Xiaolong, Zhang. 2020-08-27. GraphFederator: Federated Visual Analysis for Multi-party Graphs. https://arxiv.org/abs/2008.11989

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