What is recorded here?
Adds degree centrality, shortest-path distance attention biases and path-based edge encodings to a Transformer graph representation model.
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Scientific scope
Eight original authors; internship/correspondence marks are not equal-contribution marks. Source-specific expressivity and benchmark statements do not establish all-graph universality or linear-time attention. Path selection and graph preprocessing have costs; the architectural contribution is distinct from independently established application outcomes.
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People and systems
Chengxuan Ying; Tianle Cai; Shengjie Luo; Shuxin Zheng; Guolin Ke; Di He; Yanming Shen; Tie-Yan Liu
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English catalogue narrative
Graphormer structural encodings for graph Transformers
Adds degree centrality, shortest-path distance attention biases and path-based edge encodings to a Transformer graph representation model.
Scientific scope
Eight original authors; internship/correspondence marks are not equal-contribution marks. Source-specific expressivity and benchmark statements do not establish all-graph universality or linear-time attention. Path selection and graph preprocessing have costs; the architectural contribution is distinct from independently established application outcomes.
People and systems
Chengxuan Ying; Tianle Cai; Shengjie Luo; Shuxin Zheng; Guolin Ke; Di He; Yanming Shen; Tie-Yan Liu