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Research Paper: PostDeg Enhances GNNs by Optimizing LayerNorm Scalar Placement

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A new research paper titled "PostDeg: Placement Beats Parameterization in LayerNorm GNNs" has been submitted to arXiv. The paper identifies that the placement of a positive per-node scalar within LayerNorm-based Graph Neural Networks (GNNs) significantly impacts their ability to retain topological signals. The authors propose "PostDeg," a parameter-free method that inserts this scalar after LayerNorm, demonstrating substantial performance gains on tasks like influence maximization and network dismantling compared to standard LayerNorm backbones. AI

RANK_REASON The cluster contains a research paper detailing a new method for improving Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Research Paper: PostDeg Enhances GNNs by Optimizing LayerNorm Scalar Placement

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The cluster contains a research paper detailing a new method for improving Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. arXiv cs.LG TIER_1 English(EN) · Yash Tomar, Aryav Das ·

    PostDeg: Placement Beats Parameterization in LayerNorm GNNs

    arXiv:2606.14022v1 Announce Type: new Abstract: LayerNorm-based GNNs routinely erase the topology signals (degree, centrality, $k$-core) that node-selection policies should depend on, but the literature has not located where in the residual block the erasure happens. We answer th…

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