GNNs with ReLU activation are more expressive than bounded activations

A new research paper explores the computational expressiveness of Graph Neural Networks (GNNs) by analyzing a declarative language called MPLang. The study differentiates between GNNs with and without activation functions, demonstrating that bounded activations yield the same expressive power. Notably, the research proves that GNNs utilizing the ReLU activation function are strictly more powerful for numerical queries than those restricted to eventually constant activations when linear layers are present. AI

IMPACT This research clarifies the theoretical expressiveness of GNNs, potentially guiding future model design and evaluation.

RANK_REASON Academic paper published on arXiv detailing theoretical aspects of GNN computation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Graph Neural Networks
• Matthias Lanzinger
• MPLang
• ReLU

• paper
• model release

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