Research map / Graph learning / GNN architectures
A Closer Look at Graph Transformers: Cross-Aggregation and Beyond
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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Analyzes Graph Transformers' mechanisms for using topology information, focusing on integrating GNN modules and positional-encoding-based node attribute modulation.
Problem
Graph Transformers leverage topology via GNN modules or positional encodings, but the underlying mechanism driving their effectiveness is unclear.
Approach
Examines the mechanisms behind GT effectiveness, with the title pointing to cross-aggregation between graph and attention components; the abstract is truncated so the precise method is not stated.
Closest papers by meaning
- Less is More: on the Over-Globalizing Problem in Graph Transformers (ICML 2024 · similarity 0.90)
- Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-Based Similarity (AAAI 2024 · similarity 0.90)
- Relieving the Over-Aggregating Effect in Graph Transformers (NeurIPS 2025 · similarity 0.89)
- Graph External Attention Enhanced Transformer (ICML 2024 · similarity 0.89)
- Generalizable Insights for Graph Transformers in Theory and Practice (NeurIPS 2025 · similarity 0.89)
- Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products (ICML 2024 · similarity 0.89)
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