awesome-graph-transformer

Why we included this project

Anyone digging into graph representation learning will get real value from this reading list. It gathers graph transformer papers and sorts them by the technique they use, so you can trace how absolute positional encodings, relative encodings, and GNN-as-encoder designs developed without hunting through Google Scholar. The maintainers keep recategorizing entries as the field moves, which means the list stays honest about which ideas actually caught on. It is a bibliography rather than software you run, so its payoff is what it teaches you about the state of the art, plus the survey papers it links to. For someone new to graph transformers or preparing a paper in the area, it is a solid place to start.