FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding

Abstract

Neighborhood aggregation algorithms, represented as graph convolutional networks, have attained non-negligible success in numerous topological structure-based scenarios with the assumption that the topological structure of the given graph is pre-defined and relatively small. However, a real-world graph generally is a super large graph consisting of many small graphs that are interconnected and overlapping. Which makes graph embedding in real-life industries, by nature, fall into the federated learning scheme. While current graph-based algorithms are only able to capture the individual topology of each natural graph, learning the complete structural information of the merged large graph remains challenging due to the unsustainable computational cost of graph convolutional operations. We propose a tailored federated graph embedding framework to learn the intact structural information of the numerous inherently linked small-scale graphs and the embedding of each node. We leverage graphs with around two and a half million nodes to validate the effectiveness and the correctness of the proposed framework.