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Transformers over Directed Acyclic Graphs

NeurIPS 2023 · Graph learning › Graph self-supervised learning · approach: Semi-supervised node representation learning · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add reachability-based attention (DAGRA) (ablation)Transformer · ogbg-code2Test F1: 0.1670 -> 0.1895Helped
Add reachability-based attention (DAGRA) (ablation)Transformer · NARMSE: 0.284 -> 0.253Helped
Add reachability-based attention (DAGRA) (ablation)Transformer · Self-citationAP: 0.573 -> 0.638Helped
Add depth positional encoding (DAGPE) (ablation)Transformer · NARMSE: 0.263 -> 0.253Helped
Add depth positional encoding (DAGPE) (ablation)Transformer · Self-citationAP: 0.594 -> 0.638Helped
Add depth positional encoding (DAGPE) (ablation)Transformer · ogbg-code2Test F1: 0.1879 -> 0.1895No clear effect
Replace DAGPE with RWPE (ablation)DAG+Transformer · NARMSE: 0.253 -> 0.267Hurt
Replace DAGPE with LapPE (ablation)DAG+Transformer · NARMSE: 0.253 -> 0.271Hurt
Add shortest-path distance bias (ablation)DAG+Transformer · Self-citationAP: 0.638 -> 0.639No clear effect
Add edge-direction bias (ablation)DAG+Transformer · ogbg-code2Test F1: 0.1895 -> 0.1870Hurt
Increase receptive-field radius from k=1 to k=∞ (ablation)DAG+TF · ogbg-code2Test F1 score: 0.1724 -> 0.1879Helped
Increase receptive-field radius from k=1 to k=∞ (ablation)DAG+SAT · ogbg-code2Test F1 score: 0.1533 -> 0.2018Helped

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