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This paper introduces addressable and cardinality-preserving global memory for message-passing neural networks via cross-attention slots, addressing the finite-capacity bottleneck of virtual nodes and improving performance on multiplicity-aware tasks.
This paper introduces HMH, a hierarchical multi-scale Graph Neural Network framework designed to address oversmoothing and oversquashing in heterophilous graphs. It utilizes spectral filters with Haar bases to achieve scalable learning and improved performance on node and graph classification tasks.