Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space

ArXi:2501.15461v4 Announce Type: replace Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods. In this paper, we