125 citations · 171 across the 6 of their papers we have counts for
6 papers
Fast and Accurate Network Embeddings via Very Sparse Random Projection
Haochen Chen, Syed Fahad Sultan, Yingtao Tian +2
We present FastRP, a scalable and performant algorithm for learning distributed node representations in a graph. FastRP is over 4,000 times faster than state-of-the-art methods suc…
Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation
Yun He, Haochen Chen, Ziwei Zhu +1
We propose PsiRec, a novel user preference propagation recommender that incorporates pseudo-implicit feedback for enriching the original sparse implicit feedback dataset. Three of…
Enhanced Network Embeddings via Exploiting Edge Labels
Haochen Chen, Xiaofei Sun, Yingtao Tian +3
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tas…
A Tutorial on Network Embeddings
Haochen Chen, Bryan Perozzi, Rami Al-Rfou +1
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on gr…
Learning to Represent Bilingual Dictionaries
Muhao Chen, Yingtao Tian, Haochen Chen +3
Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages. However, many applications, such as cross-lingual seman…
HARP: Hierarchical Representation Learning for Networks
Haochen Chen, Bryan Perozzi, Yifan Hu +1
We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by co…