Title: SpecTRA: Spectral Transformer for Graph Representation Learning. Abstract: Transformers have recently been applied in the more generic domain of graphs. For the same, researchers proposed various positional and structural encoding schemes to overcome the limitation of transformers in modeling the positional invariance in graphs and graph topology. Some of these encoding techniques use the spectrum of the graph. In addition to graph topology, graph signals could be multi-channeled and contain heterogeneous information. We argue that transformers cannot model multichannel signals inherently spread over the graph spectrum. To this end, we propose SpecTRA, a novel approach that induces a spectral module into the transformer architecture to enable decomposition of graph spectrum and selectively learn useful information akin to filtering in the frequency domain. Results on standard benchmark datasets show that the proposed method performs comparably or better than existing transformer and GNN based architectures.