Learning Topological Representation for Networks via Hierarchical Sampling

@article{Fu2019LearningTR,
  title={Learning Topological Representation for Networks via Hierarchical Sampling},
  author={Guoji Fu and Chengbin Hou and Xin Yao},
  journal={2019 International Joint Conference on Neural Networks (IJCNN)},
  year={2019},
  pages={1-8}
}
  • Guoji Fu, Chengbin Hou, Xin Yao
  • Published 2019
  • Computer Science, Mathematics
  • 2019 International Joint Conference on Neural Networks (IJCNN)
The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network and they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing… Expand
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