14 citations · 15 across the 4 of their papers we have counts for
5 papers · 1 filter
GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale Recommendations
Zhuoning Guo, Guangxing Chen, Qian Gao +4
Web recommendations provide personalized items from massive catalogs for users, which rely heavily on retrieval stages to trade off the effectiveness and efficiency of selecting a…
Against Multifaceted Graph Heterogeneity via Asymmetric Federated Prompt Learning
Zhuoning Guo, Ruiqian Han, Hao Liu
Federated Graph Learning (FGL) aims to collaboratively and privately optimize graph models on divergent data for different tasks. A critical challenge in FGL is to enable effective…
Labor Migration Modeling through Large-scale Job Query Data
Zhuoning Guo, Le Zhang, Hengshu Zhu +3
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, exis…
Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting
Zhuoning Guo, Hao Liu, Le Zhang +3
Labor market forecasting on talent demand and supply is essential for business management and economic development. With accurate and timely forecasts, employers can adapt their re…
HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning
Zhuoning Guo, Duanyi Yao, Qiang Yang +1
Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable effo…