Geometric Graph Representation Learning via Maximizing Rate Reduction

@article{Han2022GeometricGR,
  title={Geometric Graph Representation Learning via Maximizing Rate Reduction},
  author={Xiaotian Han and Zhimeng Jiang and Ninghao Liu and Qingquan Song and Jundong Li and Xia Hu},
  journal={Proceedings of the ACM Web Conference 2022},
  year={2022}
}
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation learning methods (e.g., based on random walk and contrastive learning) are limited to maximizing the local similarity of connected nodes. Such pair-wise learning schemes could fail to capture the global distribution of representations, since it has no explicit constraints on the global geometric properties of… 
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