arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop features, which makes them highly scalable. A puzzle in this regime is that more complex hop aggregators do not reliably outperform simpler ones: on many benchmarks, a plain MLP-based aggregator matches or beats hop-attention variants. We revisit this behavior from a graph-filter perspective. Over a precomputed diffusion basis, existing PPGNNs differ mainly in how filter coefficients are shared ac
This paper re-evaluates and proposes a novel architecture for pre-propagation graph neural networks as research continues to push the boundaries of scalable and efficient AI models.
Improved GNN architectures can lead to more efficient and powerful AI systems, impacting fields like drug discovery, material science, and social network analysis, and potentially reducing computational resource demands.
The proposed 'Node-Channel Mixtures' architecture offers a new method for aggregation within PPGNNs, potentially leading to more robust and performant models compared to existing approaches.
- · AI researchers
- · GNN developers
- · Cloud computing providers
- · Industries utilizing GNNs
- · Developers of less efficient GNN architectures
More sophisticated and scalable graph neural networks become available for a broader range of applications.
Reduced computational costs for certain AI tasks as more efficient GNNs are adopted, driving further AI innovation.
New breakthroughs in scientific fields by leveraging advanced GNNs to model complex systems more accurately.
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Read at arXiv cs.LG