Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited message-passing pathways. This bottleneck limits their ability to capture essential global context and decreases their performance, particularly in dense and heterophilic regions of graphs. To address this issue, we propose a novel graph learning framework that enriches node embeddings via cross-attentive cohesive subgraph representations to mitigate the impact of excessive long-range dependencies. This framework enhances the node representation by emphasizing cohesive st...
| Authors | Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 March 2026 |
| Last updated at source | 2 April 2026 |
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