3 citations · 4 across the 4 of their papers we have counts for
4 papers
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…
Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification
Xiaoye Tan, Rui Yan, Chongyang Tao +1
Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic express…
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Shupeng Gui, Xiangliang Zhang, Shuang Qiu +3
Graph embedding is a central problem in social network analysis and many other applications, aiming to learn the vector representation for each node. While most existing approaches…
Differentially Private Search Log Sanitization with Optimal Output Utility
Yuan Hong, Jaideep Vaidya, Haibing Lu +1
Web search logs contain extremely sensitive data, as evidenced by the recent AOL incident. However, storing and analyzing search logs can be very useful for many purposes (i.e. inv…