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Found 3 papers, 0 papers with code
Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks
To address these challenges, a federated graph learning approach involving multiple charging stations is proposed to collaboratively train a more generalized deep learning model for demand forecasting while capturing spatial correlations among various stations and enhancing robustness against potential attacks.
Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).
DBRec: Dual-Bridging Recommendation via Discovering Latent Groups
In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations.
