Structural hierarchy-enhanced network representation learning

Cheng Te Li, Hong Yu Lin

研究成果: Article同行評審


Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP). However, existing NRL methods neither properly identify neighbor nodes that should be pushed together and away in the embedding space, nor model coarse-grained community knowledge hidden behind the network topology. In this paper, we propose a novel NRL framework, Structural Hierarchy Enhancement (SHE), to deal with such two issues. The main idea is to construct a structural hierarchy from the network based on community detection, and to utilize such a hierarchy to perform level-wise NRL. In addition, lower-level node embeddings are passed to higher-level ones so that community knowledge can be aware of in NRL. Experiments conducted on benchmark network datasets show that SHE can significantly boost the performance of NRL in both tasks of NC and LP, compared to other hierarchical NRL methods.

頁(從 - 到)1-11
期刊Applied Sciences (Switzerland)
出版狀態Published - 2020 十月 2

All Science Journal Classification (ASJC) codes

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程


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