LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning

@article{Xu2022LiftPoolLG,
  title={LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning},
  author={Mingxing Xu and Wenrui Dai and Chenglin Li and Junni Zou and Hongkai Xiong},
  journal={ArXiv},
  year={2022},
  volume={abs/2204.12881}
}
Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e. , selecting the top-ranked nodes and removing the rest nodes to construct a coarsened graph representation. However, local structural information of the removed nodes would be inevitably dropped in these methods, due to the inherent coupling of nodes (location) and their features (signals… 
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