zhengwang100 / RSDNE

RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding. AAAI18

RSDNE

RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding. AAAI18. This is a shallow method for the problem of Zero-shot Graph Embedding (ZGE), i.e., graph embeddings when labeled data cannot cover all classes.

Usage (abstract):

% split the training and testing nodes

[X_train_nodes, Y_train, X_test_nodes, Y_test] = split_train_test_by_class(nodes, Y, train_rate=0.3) ;

% build the completely-imbalanced label setting

removedlist = [3,6] ;

[ X_zsl, Y_zsl ] = remove_classes( X_train_nodes, Y_train, removedlist ) ;

% run our algorithm

U = RSDNE(G, X_zsl, Y_zsl, lowRank, alpha, lambda, learnRate, k) ;

Citing

If you find RSDNE useful in your research, please cit our paper, thx:

@InProceedings{wang2018rsdne,
  title={{RSDNE}: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network Embedding},
  author={Wang, Zheng and Ye, Xiaojun and Wang, Chaokun and Wu, YueXin and Wang, Changping and Liang, Kaiwen},
  booktitle={AAAI},
  pages={475--482},
  year={2018}
}