A new method for node classification in multiplex graphs adapts to mixed homophily and heterophily per dimension via compatibility matrices and Chebyshev-approximated product filters optimized with proximal gradients.
Beyond redundancy: Information-aware unsuper- vised multiplex graph structure learning.Advances in Neural Information Processing Systems, 37:31629–31658
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Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
A new method for node classification in multiplex graphs adapts to mixed homophily and heterophily per dimension via compatibility matrices and Chebyshev-approximated product filters optimized with proximal gradients.