SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Guo, Dongxin Wu, Jikun Yiu, Siu Ming |
| author_facet | Guo, Dongxin Wu, Jikun Yiu, Siu Ming |
| contents | Graph transformers achieve strong results on molecular and long-range reasoning tasks, yet remain hampered by over-smoothing (the progressive collapse of node representations with depth) and attention entropy degeneration. We observe that these pathologies share a root cause with attention sinks in large language models: softmax attention's sum-to-one constraint forces every node to attend somewhere, even when no informative signal exists. Motivated by recent findings that element-wise sigmoid gating eliminates attention sinks in large language models, we propose SigGate-GT, a graph transformer that applies learned, per-head sigmoid gates to the attention output within the GraphGPS framework. Each gate can suppress activations toward zero, enabling heads to selectively silence uninformative connections. On five standard benchmarks, SigGate-GT matches the prior best on ZINC (0.059 MAE) and sets new state-of-the-art on ogbg-molhiv (82.47% ROC-AUC), with statistically significant gains over GraphGPS across all five datasets ($p < 0.05$). Ablations show that gating reduces over-smoothing by 30% (mean relative MAD gain across 4-16 layers), increases attention entropy, and stabilizes training across a $10\times$ learning rate range, with about 1% parameter overhead on OGB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17324 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention Guo, Dongxin Wu, Jikun Yiu, Siu Ming Machine Learning Artificial Intelligence 68T07, 68R10 I.2.6; I.5.1; G.2.2 Graph transformers achieve strong results on molecular and long-range reasoning tasks, yet remain hampered by over-smoothing (the progressive collapse of node representations with depth) and attention entropy degeneration. We observe that these pathologies share a root cause with attention sinks in large language models: softmax attention's sum-to-one constraint forces every node to attend somewhere, even when no informative signal exists. Motivated by recent findings that element-wise sigmoid gating eliminates attention sinks in large language models, we propose SigGate-GT, a graph transformer that applies learned, per-head sigmoid gates to the attention output within the GraphGPS framework. Each gate can suppress activations toward zero, enabling heads to selectively silence uninformative connections. On five standard benchmarks, SigGate-GT matches the prior best on ZINC (0.059 MAE) and sets new state-of-the-art on ogbg-molhiv (82.47% ROC-AUC), with statistically significant gains over GraphGPS across all five datasets ($p < 0.05$). Ablations show that gating reduces over-smoothing by 30% (mean relative MAD gain across 4-16 layers), increases attention entropy, and stabilizes training across a $10\times$ learning rate range, with about 1% parameter overhead on OGB. |
| title | SigGate-GT: Taming Over-Smoothing in Graph Transformers via Sigmoid-Gated Attention |
| topic | Machine Learning Artificial Intelligence 68T07, 68R10 I.2.6; I.5.1; G.2.2 |
| url | https://arxiv.org/abs/2604.17324 |