activity
20182022
most citedDeep Active Learning for Anchor User Prediction

30 citations · 60 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2022★ 9 cited

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

Chuang Liu, Yibing Zhan, Jia Wu +5

Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation. As an essential component of the archite…

cs.CV2022

Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap

Beihang Song, Jing Li, Shan Xue +4

Oriented object detection is a crucial task in computer vision. Current top-down oriented detection methods usually directly detect entire objects, and not only neglecting the auth…

cs.DB2020★ 8 cited

Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications

Chaochao Chen, Jamie Cui, Guanfeng Liu +2

Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However,…

cs.LG2020★ 11 cited

A Deep Framework for Cross-Domain and Cross-System Recommendations

Feng Zhu, Yan Wang, Chaochao Chen +3

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender system…

cs.SI2020

Opinion Maximization in Social Trust Networks

Pinghua Xu, Wenbin Hu, Jia Wu +1

Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to…

cs.LG2019★ 30 cited

Deep Active Learning for Anchor User Prediction

Anfeng Cheng, Chuan Zhou, Hong Yang +4

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consi…

cs.LG2018★ 2 cited

TLR: Transfer Latent Representation for Unsupervised Domain Adaptation

Pan Xiao, Bo Du, Jia Wu +3

Domain adaptation refers to the process of learning prediction models in a target domain by making use of data from a source domain. Many classic methods solve the domain adaptatio…