Learning effective feature representation against user privacy protection on social networks

Cheng Te Li, Zi Yun Zeng

Research output: Contribution to journalArticlepeer-review


Users pay increasing attention to their data privacy in online social networks, resulting in hiding personal information, such as profile attributes and social connections. While network representation learning (NRL) is widely effective in social network analysis (SNA) tasks, it is essential to learn effective node embeddings from privacy-protected sparse and incomplete network data. In this work, we present a novel NRL model to generate node embeddings that can afford data incompleteness coming from user privacy protection. We propose a structure-attribute enhanced matrix (SAEM) to alleviate data sparsity and develop a community-cluster informed NRL method, c2n2v, to further improve the quality of embedding learning. Experiments conducted across three datasets, three simulations of user privacy protection, and three downstream SNA tasks exhibit the promising performance of our NRL model SAEM+c2n2v.

Original languageEnglish
Article number4835
JournalApplied Sciences (Switzerland)
Issue number14
Publication statusPublished - 2020 Jul

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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