• The paper
• More details in my blog article

visualzing 20newsgroup data

• you can also play with the embedding interactively

label embedding from stackexchange.datascience (deep-learning as an example)

• you can also play with the embedding interactively

• core.py

20newsgroup visualization

• 20newsgroup_train.py: train for 20newsgroup dataset
• 20newsgroup_viz.py: visualization using sklearn.manifold.TSNE
• 20newsgroup_tensorboard_embedding.py: produce the embedding files for tensorboard projector, which is more interactive
  • you can also play with it here using trained embeddings

link prediction

• link_prediction.py: train (including grid search) and test

stackexchange label visualization

• stackexchange_train.py: train for the stackexchange label cooccurence graph
• stackexchange_label_embedding.py: produce the embedding files for tensorboard projector
  • you can also play with it here using trained embeddings

• suanrong/SDNE
• palash1992/GEM