Search Results for author:
Found 6 papers, 2 papers with code
UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language
Graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to transfer learned knowledge to different domains.
Coordinating Supply, Demand, and Repair Resources for Optimal Postdisaster Operation of Interdependent Electric Power and Natural Gas Distribution Systems
In this paper, we focus on the interdependent electric power and natural gas distribution systems (IENDS) and propose a comprehensive "supply - demand - repair" strategy to help the IENDS tide over the emergency periods after disasters by coordinating various emergency resources.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
Robust Survivability-Oriented Scheduling of Separable Mobile Energy Storage and Demand Response for Isolated Distribution Systems
Extreme circumstances in which a local distribution system is electrically isolated from the main power supply may not always be avoidable.
Scheduling of Separable Mobile Energy Storage Systems with Mobile Generators and Fuel Tankers to Boost Distribution System Resilience
Mobile energy resources (MERs) have been shown to boost DS resilience effectively in recent years.
