DsmNet substitutes Laplacian matrices with approximated doubly stochastic matrices in GNNs, using Neumann truncation and residual mass compensation to achieve O(K|E|) efficiency and bound Dirichlet energy decay for reduced over-smoothing.
Doubly stochastic matrices and modified laplacian matrices of graphs
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Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks
DsmNet substitutes Laplacian matrices with approximated doubly stochastic matrices in GNNs, using Neumann truncation and residual mass compensation to achieve O(K|E|) efficiency and bound Dirichlet energy decay for reduced over-smoothing.