graph transformers
Graph transformers are a type of neural network designed to work with graph-structured data by utilizing attention mechanisms to weigh the importance of different parts of the graph, enabling the model to capture complex relationships between nodes and edges.
- A Closer Look at Graph Transformers: Cross-Aggregation and Beyond
- FoGE: Fock Space inspired encoding for graph prompting
- GMV: A Unified and Efficient Graph Multi-View Learning Framework
- Generalizable Insights for Graph Transformers in Theory and Practice
- On Logic-based Self-Explainable Graph Neural Networks
- Rethinking Tokenized Graph Transformers for Node Classification
- Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
- Unifying and Enhancing Graph Transformers via a Hierarchical Mask Framework