Skip to content

Joint Autoencoders for Multi-task Network Embedding

Notifications You must be signed in to change notification settings

fatemehsrz/JAME

Repository files navigation

Fatemeh Salehi Rizi and Michael Granitzer. "Multi-task Network Embedding with Adaptive Loss Weighting." In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2020.

How to run

  • To generate embeddings run main.py

Python Files

  • main.py: generates embeddings
  • loss_weighting: it's an adaptive loss weighting layer in neural networks
  • link_pred: link prediction
  • classify: node classification
  • att_infer: attribute prediction

Folders

  • Cora: Cora Dataset
  • Citeseer: Citeseer Dataset
  • PubMed: PubMed Dataset
  • results: contains result files
  • loss: contains loss values and task weights

Acknowledgement

The presented work was developed within the Provenance Analytics project funded by the German Federal Ministry of Education and Research, grant agreement number 03PSIPT5C.

About

Joint Autoencoders for Multi-task Network Embedding

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages