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.
Be- yond homophily in graph neural networks: Current limitations and effective designs.Advances in Neural Information Processing Systems, 33:7793–7804
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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.