  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.
