@article{noauthororeditor,
  abstract = {Node  embedding  has  recently  shown  state-of-the-art  performance  in  various  network  analysis  tasks.  However,most  of  the  existing  node  embedding  methods  do  not  considerthe  uncertainty  of  the  input  data,  which  is  often  the  case  inpractice. This work offers an empirical evaluation of the typicalnode  embedding  methods  when  applied  on  uncertain  networks.Precisely,  we  examine  the  performance  of  embedding  vectorsobtained  by  these  methods  in  a  set  of  downstream  tasks.  Tothis  end,  we  employ  a  wide  range  of  uncertain  networks  andtraditional prepossessing techniques for dealing with uncertainty.Our  findings suggest  that  the existing  node embedding  methodsperform  practically  well  on  networks  with  uncertainty  once  thenetwork  data  is  appropriately  prepossessed.},
  added-at = {2021-02-10T12:21:19.000+0100},
  author = {Hoang, Eric and Zerr, Sergej and Hoang, Tuan ahn},
  biburl = {https://www.bibsonomy.org/bibtex/259b3c33921be83d9beb3ead900ae701a/zerr},
  interhash = {dc13a5c266046306d9c6e21188d19980},
  intrahash = {59b3c33921be83d9beb3ead900ae701a},
  journal = {IEEE BigDATA},
  keywords = {ROXANNE myown},
  timestamp = {2021-02-10T12:21:19.000+0100},
  title = {On Node Embedding of Uncertain Networks},
  year = 2020
}