6 papers
Blackknife: Query-Free Black-Box Rewiring Attacks on Heterogeneous Graph Neural Networks
Honglin Gao, Junhao Ren, Lan Zhao +4
Heterogeneous graph neural networks (HGNNs) perform well on graphs with multiple node and relation types, but their robustness to attacks with restricted structural access remains…
Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids
Junhao Ren, Honglin Gao, Lan Zhao +3
Uncertainties in renewable generation and demand dynamics challenge day-ahead scheduling. To enhance renewable penetration and maintain intra-day balance, we develop a multi-agent…
Multi-agent Reinforcement Learning-based Joint Design of Low-Carbon P2P Market and Bidding Strategy in Microgrids
Junhao Ren, Honglin Gao, Sijie Wang +5
The challenges of the uncertainties in renewable energy generation and the instability of the real-time market limit the effective utilization of clean energy in microgrid communit…
HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Honglin Gao, Lan Zhao, Junhao Ren +2
Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less s…
HeteroBA: A Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Honglin Gao, Xiang Li, Lan Zhao +1
Heterogeneous graph neural networks (HGNNs) have recently drawn increasing attention for modeling complex multi-relational data in domains such as recommendation, finance, and soci…
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
Xiang Li, Lan Zhao, Junhao Ren +4
Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involve…