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 that question: a positive per-node scalar inserted before LayerNorm is divided out up to a stabilizer term, while the same scalar inserted after LayerNorm reaches the score head as representation magnitude. The surviving slot is the post-LayerNorm position. We instantiate it with PostDeg, a parameter-free post-LayerNorm
This research emerges now as the field of Graph Neural Networks (GNNs) matures and researchers work to overcome foundational limitations in areas like preserving topological signals, which are crucial for effective GNN performance.
Improving GNN architectures by correctly preserving topological information will lead to more robust and accurate AI models, potentially accelerating advancements in various applications that rely on complex relational data.
The understanding of how LayerNorm in GNNs erases topological signals and a proposed solution now offer a path to design more effective and topology-aware GNN architectures.
- · AI researchers (GNNs)
- · Deep learning practitioners
- · Data scientists
- · GNN-dependent applications
- · Inefficient GNN architectures
- · Organizations relying on suboptimal GNN models
More accurate and efficient GNN models will be developed and deployed across various industries.
Improved GNNs could unlock new capabilities in drug discovery, social network analysis, and recommendation systems.
The enhanced performance of GNNs might contribute to the broader development of more sophisticated and generalizable AI agents.
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Read at arXiv cs.LG