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Found 3 papers, 2 papers with code
Implicit vs Unfolded Graph Neural Networks
It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges.
Scaling Up Graph Neural Networks Via Graph Coarsening
Scalability of graph neural networks remains one of the major challenges in graph machine learning.
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.
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