1 citations · 1 across the 2 of their papers we have counts for
4 papers
GMoPE:A Prompt-Expert Mixture Framework for Graph Foundation Models
Zhibin Wang, Zhixing Zhang, Shuqi Wang +2
Graph Neural Networks (GNNs) have demonstrated impressive performance on task-specific benchmarks, yet their ability to generalize across diverse domains and tasks remains limited.…
Attention Beyond Neighborhoods: Reviving Transformer for Graph Clustering
Xuanting Xie, Bingheng Li, Erlin Pan +3
Attention mechanisms have become a cornerstone in modern neural networks, driving breakthroughs across diverse domains. However, their application to graph structured data, where c…
Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing
Xuanting Xie, Bingheng Li, Erlin Pan +2
Graph Neural Networks (GNNs) have become a dominant approach to learning graph representations, primarily because of their message-passing mechanisms. However, GNNs typically adopt…
One Node One Model: Featuring the Missing-Half for Graph Clustering
Xuanting Xie, Bingheng Li, Erlin Pan +3
Most existing graph clustering methods primarily focus on exploiting topological structure, often neglecting the ``missing-half" node feature information, especially how these feat…