How Expressive are Transformers in Spectral Domain for Graphs?
This work addresses the inadequacy of transformers in graph learning for researchers and practitioners, offering an incremental improvement by extending spectral analysis to transformers.
The paper tackled the problem of transformers' limited expressiveness for graph representation learning by proposing FeTA, a framework that performs attention over the graph spectrum, resulting in homogeneous performance gains across all tasks on standard benchmarks compared to vanilla transformers.
The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning. To understand this inadequacy, there is a need to investigate if spectral analysis of the transformer will reveal insights into its expressive power. Similar studies already established that spectral analysis of Graph neural networks (GNNs) provides extra perspectives on their expressiveness. In this work, we systematically study and establish the link between the spatial and spectral domain in the realm of the transformer. We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space. Therefore, we propose FeTA, a framework that aims to perform attention over the entire graph spectrum (i.e., actual frequency components of the graphs) analogous to the attention in spatial space. Empirical results suggest that FeTA provides homogeneous performance gain against vanilla transformer across all tasks on standard benchmarks and can easily be extended to GNN-based models with low-pass characteristics (e.g., GAT).