Representation Learning for Scale-free Networks

@article{Feng2018RepresentationLF,
  title={Representation Learning for Scale-free Networks},
  author={Rui Feng and Yang Yang and Wenjie Hu and Fei Wu and Yueting Zhuang},
  journal={ArXiv},
  year={2018},
  volume={abs/1711.10755}
}
Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored. Scale-free property depicts the fact that vertex degrees follow a heavy-tailed distribution (i.e., only a few vertexes… 

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