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cs.LG2025
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…
cs.LG2024★ 3 cited
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…