arXiv:2403.11199v2 Announce Type: replace-cross Abstract: Unitarity is a useful principle for stabilizing deep neural networks, but in graph neural networks (GNNs) instability is induced not only by learnable parameters but also by the graph propagation operator. Motivated by this distinction, we propose Graph Unitary Message Passing (GUMP), a message-passing framework that uses a unitary propagation operator on a transformed graph to avoid graph-induced exponential decay under repeated propagation. GUMP combines (i) a graph transformation that maps an input graph to an Eulerian line-graph con
The continuous drive for more stable and performant deep learning architectures, particularly in the growing field of Graph Neural Networks (GNNs), motivates research into foundational stability principles.
This paper addresses a core instability issue in GNNs by proposing a novel unitary propagation operator, potentially leading to more robust and scalable AI models for complex relational data.
The GUMP framework offers a new methodological path for designing GNNs that can propagate information more effectively over large and complex graphs without exponential decay, opening doors for broader GNN application.
- · AI researchers
- · GNN developers
- · Industries relying on relational data (e.g., social networks, drug discovery, su
- · Existing unstable GNN architectures
- · Research without robust stability mechanisms
Improved stability and performance of Graph Neural Networks using unitary principles.
Accelerated development and adoption of GNNs in complex, real-world applications due to enhanced reliability.
New classes of AI applications become feasible, particularly those requiring deep propagation over intricate knowledge graphs or network structures.
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Read at arXiv cs.AI