1 code implementation • ACL 2022 • Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li
Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.
1 code implementation • 15 Mar 2024 • Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han
To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query.
1 code implementation • 6 Nov 2023 • Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han
Despite remarkable success in various applications, large language models (LLMs) are vulnerable to adversarial jailbreaks that make the safety guardrails void.
no code implementations • 2 Nov 2023 • Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs.
1 code implementation • NeurIPS 2023 • Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, Bo Han
To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.
no code implementations • 17 Oct 2023 • Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong liu
To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem.
1 code implementation • 15 Jun 2023 • Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han
Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored.
2 code implementations • 30 May 2022 • Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han
An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step.
2 code implementations • 5 May 2022 • Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.