Temporal Network Representation Learning via Historical Neighborhoods Aggregation

@article{Huang2020TemporalNR,
  title={Temporal Network Representation Learning via Historical Neighborhoods Aggregation},
  author={Shixun Huang and Zhifeng Bao and Guoliang Li and Yanghao Zhou and J. Shane Culpepper},
  journal={2020 IEEE 36th International Conference on Data Engineering (ICDE)},
  year={2020},
  pages={1117-1128}
}
  • Shixun Huang, Z. Bao, J. Culpepper
  • Published 30 March 2020
  • Computer Science
  • 2020 IEEE 36th International Conference on Data Engineering (ICDE)
Network embedding is an effective method to learn low-dimensional representations of nodes, which can be applied to various real-life applications such as visualization, node classification, and link prediction. Although significant progress has been made on this problem in recent years, several important challenges remain, such as how to properly capture temporal information in evolving networks. In practice, most networks are continually evolving. Some networks only add new edges or nodes… 

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