Learning Regularized Noise Contrastive Estimation for Robust Network Embedding
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DOI: 10.1109/tkde.2022.3148284
Abstract: Skip-gram models are popular in large-scale network embedding for their cost-effectiveness. The objectives of many skip-gram based methods relate to the word2vec model which closely relates to Noise Contrastive Estimation (NCE). Among existing embedding methods,… read more here.
Keywords: network; noise contrastive; nce; network embedding ... See more keywords