5 citations · 11 across the 6 of their papers we have counts for
6 papers
Automated Decision-Making on Networks with LLMs through Knowledge-Guided Evolution
Xiaohan Zheng, Lanning Wei, Yong Li +1
Effective decision-making on networks often relies on learning from graph-structured data, where Graph Neural Networks (GNNs) play a central role, but they take efforts to configur…
Superpose Task-specific Features for Model Merging
Haiquan Qiu, You Wu, Dong Li +2
Model merging enables powerful capabilities in neural networks without requiring additional training. In this paper, we introduce a novel perspective on model merging by leveraging…
Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling
Yu Liu, Shu Yang, Jingtao Ding +2
By representing knowledge in a primary triple associated with additional attribute-value qualifiers, hyper-relational knowledge graph (HKG) that generalizes triple-based knowledge…
Dual Reasoning: A GNN-LLM Collaborative Framework for Knowledge Graph Question Answering
Guangyi Liu, Yongqi Zhang, Yong Li +1
Large Language Models (LLMs) excel at intuitive, implicit reasoning. Guiding LLMs to construct thought chains can enhance their deliberate reasoning abilities, but also faces chall…
Knowledge-Enhanced Recommendation with User-Centric Subgraph Network
Guangyi Liu, Quanming Yao, Yongqi Zhang +1
Recommendation systems, as widely implemented nowadays on various platforms, recommend relevant items to users based on their preferences. The classical methods which rely on user-…
Combating Bilateral Edge Noise for Robust Link Prediction
Zhanke Zhou, Jiangchao Yao, Jiaxu Liu +6
Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less inve…