3 citations · 3 across the 5 of their papers we have counts for
5 papers
PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classification
Zhicong Cai, Yinglong Zhang, Xiaoying Hong +2
Graph neural networks have achieved strong performance in node classification by aggregating information from graph neighborhoods. However, a single aggregation mechanism may be in…
Dual Refinement Cycle Learning: Unsupervised Text Classification of Mamba and Community Detection on Text Attributed Graph
Hong Wang, Yinglong Zhang, Hanhan Guo +2
Pretrained language models offer strong text understanding capabilities but remain difficult to deploy in real-world text-attributed networks due to their heavy dependence on label…
Odin: Oriented Dual-module Integration for Text-rich Network Representation Learning
Kaifeng Hong, Yinglong Zhang, Xiaoying Hong +2
Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely on GNNs--limited by…
Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks
Mingyue Kong, Yinglong Zhang, Chengda Xu +2
Graph Neural Networks (GNNs) have shown remarkable performance in structured data modeling tasks such as node classification. However, mainstream approaches generally rely on a lar…
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
Jiawei E, Yinglong Zhang, Xuewen Xia +1
In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading…