activity
20192021
most citedAdversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection

4 citations · 8 across the 2 of their papers we have counts for

collaborators

8 papers

cs.SI20214 cited

Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection

Yuxiang Ren, Bo Wang, Jiawei Zhang +1

The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media…

cs.LG20214 cited

Label Contrastive Coding based Graph Neural Network for Graph Classification

Yuxiang Ren, Jiyang Bai, Jiawei Zhang

Graph classification is a critical research problem in many applications from different domains. In order to learn a graph classification model, the most widely used supervision co…

cs.SI2020

Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention

Yuxiang Ren, Jiawei Zhang

The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learn…

cs.LG2019

EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

Yuxiang Ren, Hao Zhu, Jiawei Zhang +2

Fraud detection is extremely critical for e-commerce business. It is the intent of the companies to detect and prevent fraud as early as possible. Existing fraud detection methods…

cs.SI2019

Scalable Heterogeneous Social Network Alignment through Synergistic Graph Partition

Yuxiang Ren, Lin Meng, Jiawei Zhang

Social network alignment has been an important research problem for social network analysis in recent years. With the identified shared users across networks, it will provide resea…

cs.LG2019

Heterogeneous Deep Graph Infomax

Yuxiang Ren, Bo Liu, Chao Huang +3

Graph representation learning is to learn universal node representations that preserve both node attributes and structural information. The derived node representations can be used…