Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (5): 83-94.doi: 10.3969/j.issn.1000-5641.202091007

• Semantic Extraction from Data • Previous Articles     Next Articles

Approaches on network vertex embedding

ZHOU Xiaoxu1, LIU Yingfeng2, FU Yingnan1, ZHU Renyu1, GAO Ming1   

  1. 1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China;
    2. Shanghai Municipal Big Data Center, Shanghai 200072, China
  • Received:2020-08-05 Published:2020-09-24

Abstract: Network is a commonly used data structure, which is widely applied in social network, communication and biological fields. Thus, how to represent network vertices is one of the difficult problems that is widely concerned in academia and industry. Network vertex representation aims at learning to map each vertex into a vector in a low-dimensional space, and simultaneously preserving the topology structure between vertices in the network. Based on the analysis of the motivation and challenges of network vertex representation, this paper analyzes and compares the mainstream methods of network vertex representation in detail, including matrix decomposition, random walk and deep learning based approaches, and finally introduces the methods to measure the performance of network vertex representation.

Key words: network embedding, random walk, matrix factorization, deep neural network

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